PV_LIB Toolbox

The PV_LIB Toolbox provides a set of well-documented functions for simulating the performance of photovoltaic energy systems. Currently there are two distinct versions (pvlib-python and PVILB for Matlab) that differ in both structure and content. Both versions were initially developed at Sandia National Laboratories but have since been offered as open-source software projects and have grown significantly from contributions from an active community of users (see list of papers that cite the packages below).

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Documentation Links:

To cite pvlib-python please reference this 2018 Publication of PVLIB in Journal of Open Source Software: pvlib python: a python package for modeling solar energy systems (22563 downloads)

Holmgren, W., C. Hansen and M. Mikofski (2018). “pvlib Python: A python package for modeling solar energy systems.” Journal of Open Source Software 3(29): 884.

Partial List of References that cite PVLIB (obtained from Google Scholar):

2022 (updated February 8, 2022)

Hu, W, Cervone, G, Merzky, A, Turilli, M, & Jha, S (2022). A New Hourly Dataset for Photovoltaic Energy Production for the Continental USA. Data in Brief, Elsevier, https://www.sciencedirect.com/science/article/pii/S2352340922000361
Smith, DE, Hughes, MD, & Borca-Tasciuc, DA (2022). Towards a standard approach for annual energy production of concentrator-based building-integrated photovoltaics. Renewable Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0960148121018759
Riaz, MH, Imran, H, Alam, H, Alam, MA, & … (2022). Crop-Specific Optimization of Bifacial PV Arrays for Agrivoltaic Food-Energy Production: The Light-Productivity-Factor Approach. IEEE Journal of …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9674806/
Yang, D, Wang, W, & Xia, X (2022). A Concise Overview on Solar Resource Assessment and Forecasting. Advances in Atmospheric Sciences, Springer, https://doi.org/10.1007/s00376-021-1372-8
Wijeratne, WMPU, Samarasinghalage, TI, Yang, RJ, & … (2022). Multi-objective optimisation for building integrated photovoltaics (BIPV) roof projects in early design phase. Applied Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0306261921016998
Bell, C (2022). Fluids Documentation., media.readthedocs.org, https://media.readthedocs.org/pdf/fluids/latest/fluids.pdf
Arens, S, Schlüters, S, Hanke, B, Maydell, K von, & … (2022). Multi-unit Japanese auction for device agnostic energy management. International Journal of …, Elsevier, https://www.sciencedirect.com/science/article/pii/S0142061521005895
Hu, W, Cervone, G, Turilli, M, Merzky, A, & Jha, S (2022). A Scalable Solution for Running Ensemble Simulations for Photovoltaic Energy. arXiv preprint arXiv …, arxiv.org, https://arxiv.org/abs/2201.06962
Lorenz, E, Guthke, P, Dittmann, A, Holland, N, & … (2022). High resolution measurement network of global horizontal and tilted solar irradiance in southern Germany with a new quality control scheme. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X21009828
Cañadillas-Ramallo, D, Moutaoikil, A, Shephard, LE, & … (2022). The influence of extreme dust events in the current and future 100% renewable power scenarios in Tenerife. Renewable Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S096014812101733X
Nespoli, A, Niccolai, A, Ogliari, E, Perego, G, Collino, E, & … (2022). Machine Learning techniques for solar irradiation nowcasting: Cloud type classification forecast through satellite data and imagery. Applied Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0306261921011600
Romero-Fiances, I, Livera, A, Theristis, M, Makrides, G, & … (2022). Impact of duration and missing data on the long-term photovoltaic degradation rate estimation. Renewable Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S096014812101404X
Habte, A (2022). Solar Radiometer Instrumentation Evaluation: Cooperative Research and Development Final Report, CRADA Number CRD-16-00619., osti.gov, https://www.osti.gov/biblio/1841135
Oh, M, Kim, CK, Kim, B, Yun, C, Kim, JY, Kang, Y, & Kim, HG (2022). Analysis of minute-scale variability for enhanced separation of direct and diffuse solar irradiance components using machine learning algorithms. Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0360544221031704
Feng, C, Zhang, J, Zhang, W, & Hodge, BM (2022). Convolutional neural networks for intra-hour solar forecasting based on sky image sequences. Applied Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0306261921016639
Visser, L, AlSkaif, T, & Sark, W van (2022). Operational day-ahead solar power forecasting for aggregated PV systems with a varying spatial distribution. Renewable Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0960148121015688
Brandi, S, Gallo, A, & Capozzoli, A (2022). A predictive and adaptive control strategy to optimize the management of integrated energy systems in buildings. Energy Reports, Elsevier, https://www.sciencedirect.com/science/article/pii/S2352484721014979
Yang, D, Yagli, GM, & Srinivasan, D (2022). Sub-minute probabilistic solar forecasting for real-time stochastic simulations. Renewable and Sustainable Energy …, Elsevier, https://www.sciencedirect.com/science/article/pii/S1364032121010078
Eggimann, S, Vulic, N, Rüdisüli, M, Mutschler, R, & … (2022). Spatiotemporal upscaling errors of building stock clustering for energy demand simulation. Energy and …, Elsevier, https://www.sciencedirect.com/science/article/pii/S0378778822000159
Paul, D, Michele, G De, Najafi, B, & Avesani, S (2022). Benchmarking clear sky and transposition models for solar irradiance estimation on vertical planes to facilitate glazed facade design. Energy and Buildings, Elsevier, https://www.sciencedirect.com/science/article/pii/S0378778821009063
Dab, K, Agbossou, K, Henao, N, Dubé, Y, Kelouwani, S, & … (2022). A compositional kernel based gaussian process approach to day-ahead residential load forecasting. Energy and …, Elsevier, https://www.sciencedirect.com/science/article/pii/S037877882100743X
Beebe, NHF (2022). A Bibliography of Publications about the Python Scripting and Programming Language., ctan.math.utah.edu.
Blum, NB, Wilbert, S, Nouri, B, Lezaca, J, Huckebrink, D, & … (2022). Measurement of diffuse and plane of array irradiance by a combination of a pyranometer and an all-sky imager. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X21010240
Zhang, G, Yang, D, Galanis, G, & Androulakis, E (2022). Solar forecasting with hourly updated numerical weather prediction. Renewable and Sustainable …, Elsevier, https://www.sciencedirect.com/science/article/pii/S1364032121010364

2021

Theristis, M., A. Livera, L. Micheli, J. Ascencio-Vásquez, G. Makrides, G. E. Georghiou and J. S. Stein (2021). “Comparative Analysis of Change-Point Techniques for Nonlinear Photovoltaic Performance Degradation Rate Estimations.” IEEE Journal of Photovoltaics 11(6): 1511-1518.
Driesse, A., M. Theristis and J. S. Stein (2021). “A New Photovoltaic Module Efficiency Model for Energy Prediction and Rating.” IEEE Journal of Photovoltaics 11(2): 527-534.
Holmgren, W, Lorenzo, T, Hansen, C, Mikofski, M, Krien, U, & … (2021). pvlib/pvlib-python: v0. 8.1., Jan
Ransome, S. (2021), VIRTUAL PVPearl Training School Brasov, Romania, http://www.steveransome.com/pubs/2021_07_PVCOST_Romania_Ransome_210706t11tobepresented.pdf
Khari, S, Ismail, ALİ, Lokman, H, & … (2021). Power loss calculation of Photovoltaics using Python. Computers and …, dergipark.org.tr, https://dergipark.org.tr/en/pub/ci/issue/64530/952567
Sinha, A, Kumar, A, Tiwari, A, & Yadav, K (2021). Analysis of Combined Effect of Temperature and Wind on Solar Power Production. Renewable Power for Sustainable …, Springer, https://doi.org/10.1007/978-981-33-4080-0_57
Ziyoitdinova, M (2021). THE ROLE OF PROBABILITY THEORY IN THE MODELING OF SEMICONDUCTOR DEVICES. Deutsche Internationale Zeitschrift für …, cyberleninka.ru, https://cyberleninka.ru/article/n/the-role-of-probability-theory-in-the-modeling-of-semiconductor-devices
Polo, J, Martín-Chivelet, N, Sanz-Saiz, C, & … (2021). Modeling soiling losses for rooftop PV systems in suburban areas with nearby forest in Madrid. Renewable Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0960148121009514
Ismoilov, U (2021). THE ROLE OF THE PYTHON PROGRAMMING LANGUAGE IN MODELING PHYSICAL PROCESSES. Deutsche Internationale Zeitschrift für zeitgenössische …, cyberleninka.ru, https://cyberleninka.ru/article/n/the-role-of-the-python-programming-language-in-modeling-physical-processes
Rinio, M (2021). PVcheck—A Software to Check Your Photovoltaic System. Energies, mdpi.com, https://www.mdpi.com/1996-1073/14/20/6757
Gündogdu, H, & Demirc, A (2021). Performance Comparison of Grey Wolf and Perturb&Observe MPPT Algorithms in Different Weather Conditions. 2021 13th International Conference on …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9677791/
Sivapriyan, R, Elangovan, D, & Lekhana, KSN (2021). Review of Python for Solar Photovoltaic Systems. Evolutionary Computing and …, Springer, https://doi.org/10.1007/978-981-15-5258-8_12
Øgaard, MB, Riise, HN, & Selj, JH (2021). Modeling Snow Losses in Photovoltaic Systems. 2021 IEEE 48th Photovoltaic …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9518886/
Velosa, N, & Pereira, L (2021). Towards pro-social load balancing in energy communities. Proceedings of the 8th ACM International …, dl.acm.org, https://doi.org/10.1145/3486611.3492235
Kempe, MD, Holsapple, D, Whitfield, K, & … (2021). Standards development for modules in high temperature micro‐environments. Progress in …, Wiley Online Library, https://doi.org/10.1002/pip.3389
Mandal, RK, & Kale, PG (2021). Assessment of different multiclass SVM strategies for fault classification in a PV system. Proceedings of the 7th International Conference on …, Springer, https://doi.org/10.1007/978-981-15-5955-6_70
Jose, S, & Itagi, RL (2021). Data Analytics in Solar Photovoltaics Power Forecasting for Smart Grid Applications. 2021 International Conference on Intelligent …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9498299/
Fonteijn, R, Nguyen, PH, Morren, J, & Slootweg, JG (2021). Baselining Flexibility from PV on the DSO-Aggregator Interface. Applied Sciences, mdpi.com, https://www.mdpi.com/1018516
Macías, J, Herrero, R, Núñez, R, & … (2021). On the effect of cell interconnection in Vehicle Integrated Photovoltaics: modelling energy under different scenarios. 2021 IEEE 48th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9518935/
Chen, S, & Li, M (2021). Improved Turbidity Estimation from Local Meteorological Data for Solar Resourcing and Forecasting Applications. Available at SSRN 3946170, papers.ssrn.com, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3946170
Prinsloo, FC, Schmitz, P, & Lombard, A (2021). Sustainability assessment framework and methodology with trans-disciplinary numerical simulation model for analytical floatovoltaic energy system planning …. Sustainable Energy Technologies and …, Elsevier, https://www.sciencedirect.com/science/article/pii/S2213138821005269
Florio, P, Peronato, G, Perera, ATD, Blasi, A Di, & … (2021). Designing and assessing solar energy neighborhoods from visual impact. Sustainable Cities and …, Elsevier, https://www.sciencedirect.com/science/article/pii/S2210670721002468
Hofmann, F, Hampp, J, Neumann, F, Brown, T, & … (2021). Atlite: a lightweight Python package for calculating renewable power potentials and time series. Journal of Open Source …, joss.theoj.org, https://doi.org/10.21105/joss.03294
Mikofski, MA, & Kharait, R (2021). Comparison of Predicted PV System Performance with SURFRAD versus TMY. 2021 IEEE 48th Photovoltaic …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9519024/
Bright, JM (2021). Introduction to synthetic solar irradiance., aip.scitation.org, https://doi.org/10.1063/9780735421820_001
Kaaya, I, & Ascencio-Vásquez, J (2021). Photovoltaic Power Forecasting Methods. Solar Radiation-Measurements …, intechopen.com, https://www.intechopen.com/online-first/photovoltaic-power-forecasting-methods
David, AY, Scott, J, Montgomery, W, & … (2021). Cloud Coverage Prediction to Improve Solar Power Management. …, scholarworks.calstate.edu, https://scholarworks.calstate.edu/downloads/xw42nf32n
Shuvro, RA, Xiong, J, & Deng, Y (2021). Spectral Correction Model Validation Using Spectroradiometer Measurements for CdTe Modules. 2021 IEEE 48th Photovoltaic …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9518893/
Anderson, K, Downs, C, Aneja, S, & … (2021). A Method for Estimating Time-Series PV Production Loss From Solar Tracking Failures. IEEE Journal of …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9627163/
Øgaard, M, Riise, HN, & Selj, JHK (2021). Estimation of snow loss for photovoltaic plants in Norway. Proceedings of the European …, duo.uio.no, https://www.duo.uio.no/handle/10852/89488
Comfort, AFOOT (2021). Modelling Perimeter Heating Demand: A Function Of Occupant Thermal Comfort., kpmb.com, https://www.kpmb.com/wp-content/uploads/2021/10/KPMB-LAB_Modelling-Perimeter-Thermal-Energy.pdf
Øgaard, MB, Aarseth, BL, Skomedal, ÅF, Riise, HN, & … (2021). Identifying snow in photovoltaic monitoring data for improved snow loss modeling and snow detection. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X21003868
Johnson, J, Jencka, L, Ortiz, T, Jones, C, Chavez, A, & … (2021). Design Considerations for Distributed Energy Resource Honeypots and Canaries.., osti.gov, https://www.osti.gov/biblio/1821540
Noord, M van, Landelius, T, & Andersson, S (2021). Snow-Induced PV Loss Modeling Using Production-Data Inferred PV System Models. Energies, mdpi.com, https://www.mdpi.com/1996-1073/14/6/1574
Schardt, J, & Heesen, H te (2021). Performance of roof-top PV systems in selected European countries from 2012 to 2019. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X21001006
Montoya, JF Dávila (2021). Diseño y evaluación mediante modelamiento de un sistema solar fotovoltaico comercial., repositorio.uniandes.edu.co, https://repositorio.uniandes.edu.co/handle/1992/51594
Wright, D, Liu, L, Parvan, L, Majumdar, Z, & … (2021). Economic analysis of a novel design of microtracked concentrating photovoltaic modules. Progress in …, Wiley Online Library, https://doi.org/10.1002/pip.3379
Anderson, K, Kemnitz, J, & Boyd, M (2021). Evaluating cell temperature models and the effect of wind speed in PV system capacity testing. 2021 IEEE 48th Photovoltaic …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9519077/
Deline, C, Anderson, K, Jordan, D, Walker, A, Desai, J, & … (2021). PV Fleet Performance Data Initiative: Performance Index-Based Analysis., osti.gov, https://www.osti.gov/biblio/1766838
Nardin, G, Domínguez, C, Aguilar, ÁF, & … (2021). Industrialization of hybrid Si/III–V and translucent planar micro‐tracking modules. Progress in …, Wiley Online Library, https://doi.org/10.1002/pip.3387
Smith, LD, & Kirschen, DS (2021). Impacts of Time-of-Use Rate Changes on the Electricity Bills of Commercial Consumers. 2021 IEEE Power & Energy Society …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9638125/
Lyden, A, Flett, G, & Tuohy, PG (2021). PyLESA: A Python modelling tool for planning-level Local, integrated, and smart Energy Systems Analysis. SoftwareX, Elsevier, https://www.sciencedirect.com/science/article/pii/S2352711021000443
Mendoza, H, Hopwood, M, & … (2021). pvOps: Improving operational assessments through data fusion. 2021 IEEE 48th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9518439/
Choné, T, Richaud, L, Rigal, B, Pellerej, R, & … (2021). New planning tool for Low Voltage photovoltaic connection-large scale experimentation. CIRED 2021-The …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9692730/
Wang, K, & Clow, GD (2021). Newly collected data across Alaska reveal remarkable biases in solar radiation products. International Journal of Climatology, Wiley Online Library, https://doi.org/10.1002/joc.6634
Кардаш, ДО, Любименко, ОМ, Кондратенко, ВГ, & … (2021). Дослідження моделі передбачення потужності, що генерується сонячною електростанцією., ea.donntu.edu.ua, http://ea.donntu.edu.ua/bitstream/123456789/33275/1/2074-2630-2021-1-73-76.pdf
Maghami, I, Sobral, VAL, Morsy, MM, Lach, JC, & … (2021). Exploring the complementary relationship between solar and hydro energy harvesting for self-powered water monitoring in low-light conditions. … Modelling & Software, Elsevier, https://www.sciencedirect.com/science/article/pii/S136481522100075X
Ernst, M, Conechado, GEJ, & Asselineau, CA (2021). Accelerating the simulation of annual bifacial illumination of real photovoltaic systems with ray tracing. Iscience, Elsevier, https://www.sciencedirect.com/science/article/pii/S2589004221016680
Westbrook, O (2021). Your P Values Are Wrong. 2021 IEEE 48th Photovoltaic Specialists …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9519076/
Bacry, E, Soares, D de Barros, Andrieux, F, & … (2021). Predicting the solar potential of rooftops using image segmentation and structured data. NIPS …, hal.archives-ouvertes.fr, https://hal.archives-ouvertes.fr/hal-03438761/file/2106.15268.pdf
Riise, HN, Øgaard, M, Zhu, J, You, CC, & … (2021). Performance analysis of a BAPV bifacial system in Norway. 2021 IEEE 48th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9518963/
Plessis, AA Du, Strauss, JM, & Rix, AJ (2021). Short-term solar power forecasting: Investigating the ability of deep learning models to capture low-level utility-scale Photovoltaic system behaviour. Applied Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0306261920317657
Pierce, BG, Braid, JL, Stein, JS, & … (2021). Solar Transposition Modeling via Deep Neural Networks With Sky Images. IEEE Journal of …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9623380/
Costa, TAC (2021). Sistema de regras para acompanhamento de performance em usinas fotovoltaicas empregando técnicas de aprendizagem de máquina., repositorio.ufc.br, https://repositorio.ufc.br/handle/riufc/61916
Jost, N, Askins, S, Dixon, R, Ackermann, M, & … (2021). Novel Interconnection Method for Micro-CPV Solar Cells. 2021 IEEE 48th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9518837/
Livera, A, Theristis, M, Koumpli, E, & … (2021). Data processing and quality verification for improved photovoltaic performance and reliability analytics. Progress in …, Wiley Online Library, https://doi.org/10.1002/pip.3349
Smith, LJ (2021). Power Output Modeling and Optimization for a Single Axis Tracking Solar Farm on Skewed Topography Causing Extensive Shading., digitalcommons.calpoly.edu, https://digitalcommons.calpoly.edu/theses/2293/
Soares, DB, Andrieux, F, Hell, B, Lenhardt, J, & … (2021). Predicting the solar potential of rooftops using image segmentation and structured data. arXiv preprint arXiv …, arxiv.org, https://arxiv.org/abs/2106.15268
Larson, DP, & Hobbs, WB (2021). Fleet-Level PV Modeling with Realistic Sub-Hourly Solar Power Variability. 2021 IEEE 48th Photovoltaic …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9518684/
Riaz, MH, Imran, H, Younas, R, & … (2021). Module technology for agrivoltaics: vertical bifacial versus tilted monofacial farms. IEEE Journal of …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9330760/
Patel, MT, Wickramaarachchi, GT, & … (2021). Machine Learning allows Synthesis and Functional Interpolation of Computational and Field-Data for Worldwide Utility-Scale PV Systems. 2021 IEEE 48th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9518432/
Azaioud, H, Knockaert, J, Vandevelde, L, & Desmet, J (2021). RE/SOURCED PILOT PROJECT: DESIGN AND POWER FLOWANALYSIS OF A LVDC BACKBONE WITH HYBRID ENERGY SYSTEM., IET, https://doi.org/10.1049/icp.2021.1737
Mahdavi, A, Wolosiuk, D, & Berger, C (2021). A bi-directional approach to building-integrated PV systems configuration. Journal of Physics …, iopscience.iop.org, https://doi.org/10.1088/1742-6596/2069/1/012114
Yao, T, Wang, J, Wu, H, Zhang, P, Li, S, Wang, Y, Chi, X, & … (2021). A photovoltaic power output dataset: Multi-source photovoltaic power output dataset with Python toolkit. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X21008070
Deline, C, Pelaez, SA, Anderson, K, Jordan, D, Perry, K, & … (2021). PV Field Performance Including Fleet and Bifacial Field Data., osti.gov, https://www.osti.gov/biblio/1778188
Chiodetti, M, Lafont, T, Gherardi, CM, & Radvanyi, E (2021). THE ROAD TOWARDS A 100% RENEWABLE ELECTRICITY MIX IN THE FRENCH ISLAND OF MIQUELON., IET, https://doi.org/10.1049/icp.2021.1754
Oh, M, Kim, JY, Kim, B, Yun, CY, Kim, CK, Kang, YH, & … (2021). Tolerance angle concept and formula for practical optimal orientation of photovoltaic panels. Renewable Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0960148120318486
Dumlao, SMG, & Ishihara, KN (2021). Weather-Driven Scenario Analysis for Decommissioning Coal Power Plants in High PV Penetration Grids. Energies, mdpi.com, https://www.mdpi.com/1996-1073/14/9/2389
Routhier, AF, Bowden, SG, Goodnick, SM, & … (2021). What is the LCOE of residential solar+ battery in the face on increasingly complex utlity rate plans?. 2021 IEEE 48th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9519070/
Deline, C, Anderson, K, Jordan, D, & … (2021). Performance Index Assessment for the PV Fleet Performance Data Initiative. 2021 IEEE 48th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9518760/
Voicu, V, Petreus, D, Cebuc, E, & … (2021). An IoT Photovoltaic Sensing System. 2021 20th RoEduNet …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9638286/
Libralato, M, Angelis, A De, D’Agaro, P, & … (2021). Multiyear hygrothermal performance simulation of historic building envelopes. … Series: Earth and …, iopscience.iop.org, https://doi.org/10.1088/1755-1315/863/1/012045
Coppitters, D, Paepe, W De, & … (2021). Robust design optimization of a renewable-powered demand with energy storage using imprecise probabilities. E3S Web of …, search.proquest.com, https://search.proquest.com/openview/491a305a63cdc9f19e54496658935319/1?pq-origsite=gscholar&cbl=2040555
Parikh, A, Perry, K, Anderson, K, & … (2021). Validation of Subhourly Clipping Loss Error Corrections. 2021 IEEE 48th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9518564/
Nair, DS, & Rajeev, T (2021). Investigation on Impact of Solar PV penetration on the Operation of Protective Relays in a Distribution System using Python. 2021 IEEE Conference on Technologies …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9467474/
Claeys, R, Azaioud, H, & Desmet, J (2021). Peak demand dynamics of low-voltage consumers under aggregation and its impact on upstream PV injection. CIRED 2021-The 26th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9692842/
Micheli, L, Smestad, GP, Bessa, JG, & … (2021). Tracking Soiling Losses: Assessment, Uncertainty, and Challenges in Mapping. IEEE Journal of …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9576825/
Meyer, D, Grimmond, S, Dueben, P, Hogan, R, & … (2021). Machine Learning Emulation of Urban Land Surface Processes. arXiv preprint arXiv …, arxiv.org, https://arxiv.org/abs/2112.11429
Marion, B (2021). Evaluation of clear-sky and satellite-derived irradiance data for determining the degradation of photovoltaic system performance. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X21004412
Mayer, MJ, & Gróf, G (2021). Extensive comparison of physical models for photovoltaic power forecasting. Applied Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0306261920316330
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Yang, D, Alessandrini, S, Antonanzas, J, & … (2020). Verification of deterministic solar forecasts. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X20303947
Besio, A Inzunza (2020). Distributional effects of net metering policies and residential solar plus behind-the-meter storage adoption., oastats.mit.edu, http://oastats.mit.edu/handle/1721.1/127171
Möller, C (2020). Speicherbedarf und Systemkosten in der Stromversorgung für energieautarke Regionen und Quartiere., dokumente.ub.tu-clausthal.de, https://dokumente.ub.tu-clausthal.de/servlets/MCRFileNodeServlet/clausthal_derivate_00001343/Db114514.pdf
Guambaña, GF Chávez (2020). Máxima transferencia de potencia de un sistema de micro generación fotovoltaica por medio de PSO y modelación dinámica por medio de Vensim., dspace.ups.edu.ec, https://dspace.ups.edu.ec/handle/123456789/19272
Nespoli, L, & Medici, V (2020). Multivariate Boosted Trees and Applications to Forecasting and Control. arXiv preprint arXiv:2003.03835, arxiv.org, https://arxiv.org/abs/2003.03835
Golubev, T (2020). Multi-physics modeling and simulation of photovoltaic devices and systems., search.proquest.com, https://search.proquest.com/openview/63ac993f14fbbab09d4adeb08d823a68/1?pq-origsite=gscholar&cbl=18750&diss=y
Tong, X, Liu, S, Yan, J, Broesicke, OA, Chen, Y, & … (2020). Thermolytic osmotic heat engine for low-grade heat harvesting: Thermodynamic investigation and potential application exploration. Applied Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0306261919318793
Durusoy, B (2020). Description and verification of a model to calculate the efficiency of a bifacial PV module using theoretical and experimental observations., open.metu.edu.tr, https://open.metu.edu.tr/bitstream/handle/11511/45370/index.pdf
Pravettoni, M (2020). Module Deployment and Energy Rating. Solar Cells and Modules, Springer, https://doi.org/10.1007/978-3-030-46487-5_10
Balderrama, JGP, Subieta, SB, Lombardi, F, & … (2020). Incorporating high-resolution demand and techno-economic optimization to evaluate micro-grids into the Open Source Spatial Electrification Tool (OnSSET). Energy for Sustainable …, Elsevier, https://www.sciencedirect.com/science/article/pii/S0973082620302180
Muschner, C (2020). An Open Source Energy Modelling Framework Comparison of OSeMOSYS and oemof., diva-portal.org, https://www.diva-portal.org/smash/record.jsf?pid=diva2:1444892
Ganchala, JD Campoverde (2020). Gestión de la demanda eléctrica mediante modelos bipartitos para una óptima respuesta a la demanda en usuarios residenciales., dspace.ups.edu.ec, https://dspace.ups.edu.ec/handle/123456789/18351
Johansson, K, & Ljungek, F (2020). ADDRESSING GRID CAPACITY THROUGH TIME SERIES: Deriving a data driven and scenario-based method for long-term planning of local grids.., diva-portal.org, https://www.diva-portal.org/smash/record.jsf?pid=diva2:1441742
Silva, M (2020). Modelado y estudio del impacto de sombras sobre paneles solares fotovoltaicos., rinfi.fi.mdp.edu.ar, http://rinfi.fi.mdp.edu.ar/handle/123456789/441
Ruiz, G Matas (2020). Desarrollo de una plataforma de datos abiertos para la estación meteorológica del Instituto de Energía Solar., oa.upm.es, https://oa.upm.es/id/eprint/63112
Brüls, M (2020). FAULT DETECTION FOR SMALL-SCALE PHOTOVOLTAIC POWER INSTALLATIONS: A Case Study of a Residential Solar Power System., diva-portal.org, https://www.diva-portal.org/smash/record.jsf?pid=diva2:1525224
Kam, MJ (2020). Driving solar: Integrating photovoltaic systems, electric vehicles, and consumer behaviour in models of smart energy systems., dspace.library.uu.nl, https://dspace.library.uu.nl/handle/1874/391795
Ismail, Y (2020). Model-based Fault Detection for Grid Connected Photovoltaic Plants from Monitoring Data., elib.dlr.de, https://elib.dlr.de/130907/1/Master_s_thesis_final.pdf
Wu, Y (2020). System Design Strategy & Energy Yield Analysis for Solar PV Application in an Urban Community with Constrained Rooftop Area., search.proquest.com, https://search.proquest.com/openview/581482703a727923afd467d575cc3e79/1?pq-origsite=gscholar&cbl=18750&diss=y
Júnior, EF Moscardini (2020). A influência do banco de dados solarimétricos e do simulador fotovoltaico no dimensionamento e na economia de combustível em usinas fotovoltaico-diesel., repositorio.ufsc.br, https://repositorio.ufsc.br/handle/123456789/215893

2019

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Perna, A, Grubbs, EK, Agrawal, R, & … (2019). Design considerations for agrophotovoltaic systems: maintaining PV area with increased crop yield. 2019 IEEE 46th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8981324/
González, M Moreno (2019). Modelado de sistemas fotovoltaicos de concentración para la biblioteca de código abierto PVLIB-Python., oa.upm.es, https://oa.upm.es/id/eprint/63168
Dyamond, WP, & Rix, AJ (2019). Detecting Anomalous Events for a Grid Connected PV Power Plant Using Sensor Data. 2019 Southern African Universities …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8704812/
DeFreitas, Z, Ramirez, A, Huang, B, & … (2019). Evaluating the accuracy of various irradiance models in detecting soiling of irradiance sensors. 2019 IEEE 46th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8981194/
Landelius, T, Andersson, S, & … (2019). Modelling and forecasting PV production in the absence of behind‐the‐meter measurements. Progress in …, Wiley Online Library, https://doi.org/10.1002/pip.3117
Holmgren, WF, Lorenzo, AT, & … (2019). Benchmark Solar Power Forecasts. 99th American …, solarforecastarbiter.org, https://solarforecastarbiter.org/assets/posters/AMS%202019%20Benchmark%20Solar%20Power%20Forecasts.pdf
Abuella, M (2019). Wind and Solar Energy Resources Modeling and Analysis., researchgate.net, https://www.researchgate.net/profile/Mohamed-Abuella/publication/335653075_Wind_and_Solar_Energy_Resources_Modeling_and_Analysis/links/5d85571592851ceb791fbb60/Wind-and-Solar-Energy-Resources-Modeling-and-Analysis.pdf
Nascimento, M, Avila, AB de, Reiser, R, Pilla, ML, & … (2019). Towards Memory Access Optimization in Quantum Computing.. EUSFLAT …, researchgate.net, https://www.researchgate.net/profile/Renata-Reiser/publication/335807320_Towards_Memory_Access_Optimization_in_Quantum_Computing/links/5ff4ade345851553a0227092/Towards-Memory-Access-Optimization-in-Quantum-Computing.pdf
Bashir, N, Chen, D, Irwin, D, & … (2019). Solar-TK: A Data-driven Toolkit for Solar PV Performance Modeling and Forecasting. 2019 IEEE 16th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9077525/
Ellis, BH, Deceglie, M, & Jain, A (2019). Automatic Detection of Clear-Sky Periods From Irradiance Data. IEEE Journal of Photovoltaics, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8727494/
Oliveira, TP, Narvaez, DI, & Villalva, MG (2019). Comparison of Irradiance Decomposition and Energy Production Methods in a Solar Photovoltaic System. International Journal of …, researchgate.net, https://www.researchgate.net/profile/Shukla-Poddar/post/How_can_I_calculate_the_solar_power_output_using_irradiance/attachment/6141a8d0181c2e4f4a886163/AS%3A1068212053610496%401631693008346/download/comparison-of-irradiance-decomposition-and-energy-production-methods-in-a-solar-photovoltaic-system.pdf
Deceglie, MG, Jordan, DC, Nag, A, & … (2019). Fleet-scale energy-yield degradation analysis applied to hundreds of residential and nonresidential photovoltaic systems. IEEE Journal of …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8598843/
Driesse, A, & Patel, N (2019). Cross-validation of PV System Simulation Software. … of the 36th European Photovoltaic Solar …, researchgate.net, https://www.researchgate.net/profile/Anton-Driesse/publication/335842590_Cross-validation_of_PV_System_Simulation_Software/links/5d84d422a6fdcc8fd6ff3180/Cross-validation-of-PV-System-Simulation-Software.pdf
Meyers, B, Tabone, M, & Kara, EC (2019). Statistical clear sky fitting algorithm. arXiv preprint arXiv:1907.08279, arxiv.org, https://arxiv.org/abs/1907.08279
Curran, AJ, Jones, CB, Lindig, S, Stein, J, & … (2019). Performance loss rate consistency and uncertainty across multiple methods and filtering criteria. 2019 IEEE 46th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8980928/
Freeman, JM, DiOrio, N, Blair, N, Guittet, D, Gilman, P, & … (2019). Improvement and validation of the system advisor model., osti.gov, https://www.osti.gov/biblio/1495693
Patel, SS, & Rix, AJ (2019). Water surface albedo modelling for floating PV plants. Europe, sasec.org.za, https://sasec.org.za/papers2019/8.pdf
Carmignani, CK (2019). CO School of Mines-Sandia-PV & Materials Tech-Python.., osti.gov, https://www.osti.gov/servlets/purl/1644475
Silva, MK (2019). Estudo de modelos matemáticos para análise da radiação solar e desenvolvimento de ferramenta para modelagem e simulação de sistemas fotovoltaicos., [sn]
Whitmire, C, Rokad, B, & Crumley, C (2019). Origami Inspired Solar Panel Design. arXiv preprint arXiv:1905.06012, arxiv.org, https://arxiv.org/abs/1905.06012
Khan, B (2019). Assessment of statistical relationship between cloud indices and relative photovoltaic (PV) production data., lutpub.lut.fi, https://lutpub.lut.fi/handle/10024/160174
Gunda, T, & Cai, M (2019). Foresee the Future: Using Machine Learning, Climate, and Site Characteristics to Predict PV Solar Plant Generation., osti.gov, https://www.osti.gov/biblio/1592882
Vivian, J, & Mazzi, N (2019). An algorithm for the optimal management of air-source heat pumps and PV systems. Journal of Physics: Conference Series, iopscience.iop.org, https://doi.org/10.1088/1742-6596/1343/1/012069
Zuiker, A (2019). A comparative study of PV simulation and machine learning models on a macrolevel and microlevel.
Camargo, L Ramirez, Nitsch, F, Gruber, K, Valdes, J, & … (2019). Potential analysis of hybrid renewable energy systems for self-sufficient residential use in Germany and the Czech Republic. Energies, mdpi.com, https://www.mdpi.com/565836
Pelaez, SA, Deline, C, Greenberg, P, & … (2019). Model and validation of single-axis tracking with bifacial PV. IEEE Journal of …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8644027/
Hansen, CW, Holmgren, WF, Tuohy, A, & … (2019). The Solar Forecast Arbiter: An Open Source Evaluation Framework for Solar Forecasting. 2019 IEEE 46th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8980713/
Kosmadakis, I, & Elmasides, C (2019). Towards performance enhancement of hybrid power supply systems based on renewable energy sources. Energy Procedia, Elsevier, https://www.sciencedirect.com/science/article/pii/S1876610218312384
Bruckman, LS (2019). Transformative Opportunities from Data Science and Big Data Analytics: Applied to Photovoltaics. The Electrochemical Society Interface, iopscience.iop.org, https://doi.org/10.1149/2.F07191if
Kim, T, Ko, W, & Kim, J (2019). Analysis and impact evaluation of missing data imputation in day-ahead PV generation forecasting. Applied Sciences, mdpi.com, https://www.mdpi.com/391836
Cormode, D, Croft, N, Hamilton, R, & … (2019). A method for error compensation of modeled annual energy production estimates introduced by intra-hour irradiance variability at PV power plants with a high DC to …. 2019 IEEE 46th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8981206/
Øgaard, MB, Skomedal, Å, & Selj, JHK (2019). Performance evaluation of monitoring algorithms for photovoltaic systems., ife.brage.unit.no, https://ife.brage.unit.no/ife-xmlui/bitstream/handle/11250/2632573/5CV.4.30_paper_MB%25C3%25982019.pdf?sequence=2
Razo, DEG, Müller, B, & Wittwer, C (2019). Ein Modellansatz zur Bestimmung von Direkt-und Diffusanteil der Einstrahlung auf die PV-Modulebene., researchgate.net, https://www.researchgate.net/profile/Dorian-Esteban-Guzman-Razo/publication/333293000_Ein_Modellansatz_zur_Bestimmung_von_Direkt-und_Diffusanteil_der_Einstrahlung_auf_die_PV-Modulebene/links/5ce56fe5458515712ebb66e6/Ein-Modellansatz-zur-Bestimmung-von-Direkt-und-Diffusanteil-der-Einstrahlung-auf-die-PV-Modulebene.pdf
Huang, J, Khan, MM, Qin, Y, & … (2019). Hybrid Intra-hour Solar PV Power Forecasting using Statistical and Skycam-based Methods. 2019 IEEE 46th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8980732/
Lee, KH, Daisuke, S, Araki, K, Yamada, N, & … (2019). Demonstration of the performance static low-concentration module using hybrid lens arrays. AIP Conference …, aip.scitation.org, https://doi.org/10.1063/1.5124208
Kosmadakis, IE, Elmasides, C, Eleftheriou, D, & … (2019). A techno-economic analysis of a pv-battery system in Greece. Energies, mdpi.com, https://www.mdpi.com/442214
Barbier, T, Blanc, P, & Saint-Drenan, YM (2019). Software correction of angular misalignments of tilted reference solar cells using clear-sky satellite open data. EU PVSEC 2019, core.ac.uk, https://core.ac.uk/download/pdf/231946055.pdf
Polo, J, Martín-Chivelet, N, Alonso-García, MC, & … (2019). Modeling IV curves of photovoltaic modules at indoor and outdoor conditions by using the Lambert function. Energy Conversion and …, Elsevier, https://www.sciencedirect.com/science/article/pii/S0196890419306405
Younas, R, Imran, H, Riaz, MH, & … (2019). Agrivoltaic farm design: Vertical bifacial vs. tilted monofacial photovoltaic panels. arXiv preprint arXiv …, researchgate.net, https://www.researchgate.net/profile/Rehan-Younas/publication/336230450_Agrivoltaic_Farm_Design_Vertical_Bifacial_vs_Tilted_Monofacial_Photovoltaic_Panels/links/5d9ad464299bf1c363fd045a/Agrivoltaic-Farm-Design-Vertical-Bifacial-vs-Tilted-Monofacial-Photovoltaic-Panels.pdf
Catalina, A, Alaíz, CM, & … (2019). Combining numerical weather predictions and satellite data for PV energy nowcasting. IEEE Transactions on …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8865555/
Chaudhari, C, Lance, T, Kimball, GM, & … (2019). PVOPEL: A Scalable Opto-Electrical Performance Model of PV systems using Ray Tracing and PVMismatch. 2019 IEEE 46th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8981230/
Coppitters, D, Paepe, W De, & Contino, F (2019). DOES THE INCLUSION OF A HYDROGEN-BASED ENERGY SYSTEM IMPROVES THE ROBUSTNESS OF THE LEVELIZED COST OF ELECTRICITY FOR AN …., energy-proceedings.org, https://www.energy-proceedings.org/wp-content/uploads/2020/02/272_Paper_0621030436.pdf
Lee, KH, Sato, D, Araki, K, Yamada, N, & … (2019). Demonstration of High Efficiency Static Low-Concentration Photovoltaic Module Using Hybrid Lens Arrays. 2019 IEEE 46th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8981268/
Camargo, LR, Gruber, K, Nitsch, F, & Dorner, W (2019). Hybrid renewable energy systems to supply electricity self-sufficient residential buildings in Central Europe. Energy Procedia, Elsevier, https://www.sciencedirect.com/science/article/pii/S1876610219301067
Headley, A, Hansen, C, & Nguyen, T (2019). Maximizing revenue from electrical energy storage paired with community solar projects in NYISO markets. 2019 North American Power …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9000262/
Braun, C (2019). A SCALABLE APPROACH FOR SPATIO-TEMPORAL ASSESSMENT OF PHOTOVOLTAIC ELECTRICITY POTENTIALS FOR BUILDING FAÇADES OF ENTIRE …. … of the Photogrammetry, Remote Sensing & …, pdfs.semanticscholar.org, https://pdfs.semanticscholar.org/1c9e/8a95ed9963b87d4dad1bd5ad2d228b25658d.pdf
Dzurick, M, Potter, BG, Holmgren, WF, & … (2019). Enhanced Photovoltaic Power Model Fidelity Using On-Site Irradiance and Degradation-Informed Performance Input. 2019 IEEE 46th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8980708/
Peláez, SA, Deline, C, Stein, JS, Marion, B, & … (2019). Effect of torque-tube parameters on rear-irradiance and rear-shading loss for bifacial PV performance on single-axis tracking systems. 2019 IEEE 46th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9198975/
Méndez, JC Julio (2019). Refinamiento e implementación de modelo computacional para predicción del comportamiento de parques solares fotovoltaicos., repositorio.uniandes.edu.co, https://repositorio.uniandes.edu.co/handle/1992/44827
Waters, M, Deline, C, Kemnitz, J, & … (2019). Suggested modifications for bifacial capacity testing. 2019 IEEE 46th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9198974/
Haakana, J, Haapaniemi, J, Lassila, J, Partanen, J, & … (2019). Electricity demand profile for residential customer 2030., cired-repository.org, https://www.cired-repository.org/handle/20.500.12455/573
Driesse, A, & Stein, J (2019). Making the Most of Module Matrix Measurements: IEC 61853-1.., osti.gov, https://www.osti.gov/servlets/purl/1641920
Kuppannagari, S, Kannan, R, & Prasanna, VK (2019). Approximate Scheduling of DERs with Discrete Complex Injections. Proceedings of the Tenth …, dl.acm.org, https://doi.org/10.1145/3307772.3328311
Stein, J, Prilliman, MJ, Stark, CT, Pelaez, SA, & Deline, C (2019). Bifacial Performance Optimization Studies using Bifacial Radiance and High Performance Computing.., osti.gov, https://www.osti.gov/servlets/purl/1641769
Platonova, EV, Toropov, AS, & … (2019). Simulation of energy input to solar panels. 2019 International Ural …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8877633/
Mikofski, MA, Darawali, R, Hamer, M, & … (2019). Bifacial performance modeling in large arrays. 2019 IEEE 46th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8980572/
Headley, A, Hansen, C, & Nguyen, TA (2019). Energy Storage Paired with Community Solar in NYISO.., osti.gov, https://www.osti.gov/servlets/purl/1642814
Headley, A, Nguyen, TA, & Byrne, RH (2019). Energy Storage Analysis for Regional Demonstration Projects.., osti.gov, https://www.osti.gov/servlets/purl/1642175
Headley, A (2019). Maximizing Storage Value in Regional Markets and the QuESt App.., osti.gov, https://www.osti.gov/servlets/purl/1645595
Shinn, A, & Browne, B (2019). Measuring Degradation of Fielded Systems on an Ongoing Basis with RdTools., osti.gov, https://www.osti.gov/biblio/1507571
Holland, N, Pang, X, Herzberg, W, & … (2019). Solar and PV forecasting for large PV power plants using numerical weather models, satellite data and ground measurements. 2019 IEEE 46th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8980496/
Meunier, S, Queval, L, Heinrich, M, & … (2019). Effect of irradiance data on the optimal sizing of photovoltaic water pumping systems. 2019 IEEE 46th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8981282/
Dyamond, WP (2019). Fault Detection and performance visualisation for a grid-connected Photovoltaic Power Plant using sensor data., scholar.sun.ac.za, http://scholar.sun.ac.za/handle/10019.1/107181
Εμμανουηλίδου, ΙΚ, & Τόλης, ΑΝ (2019). Ανάλυση επιχειρηματικών σχεδίων φωτοβολταϊκών εφαρμογών: δεδομένα εγκαταστάσεων και εκτίμηση απόδοσης., ir.lib.uth.gr, https://ir.lib.uth.gr/xmlui/bitstream/handle/11615/50027/18849.pdf?sequence=1
Heesen, H te, Herbort, V, & Rumpler, M (2019). Performance of roof-top PV systems in Germany from 2012 to 2018. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X19310023
Razo, DEG, Killinger, S, Müller, B, & Wittwer, C (2019). A Comparison of Two Models for the Separation of Direct and Diffuse Irradiance in Plane of Array., researchgate.net, https://www.researchgate.net/profile/Dorian-Esteban-Guzman-Razo/publication/336775672_A_Comparison_of_Two_Models_for_the_Separation_of_Direct_and_Diffuse_Irradiance_in_Plane_of_Array/links/5db1bf9d92851c577eba869d/A-Comparison-of-Two-Models-for-the-Separation-of-Direct-and-Diffuse-Irradiance-in-Plane-of-Array.pdf
Bennett, JA, Fuhrman, J, Brown, T, Andrews, N, Fittro, R, & … (2019). Feasibility of using sCO2 turbines to balance load in power grids with a high deployment of solar generation. Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0360544219310254
Ye, LC, Lin, HX, & Tukker, A (2019). Future scenarios of variable renewable energies and flexibility requirements for thermal power plants in China. Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0360544218321716
Miskin, CK, Li, Y, Perna, A, Ellis, RG, Grubbs, EK, & … (2019). Sustainable co-production of food and solar power to relax land-use constraints. Nature …, nature.com, https://www.nature.com/articles/s41893-019-0388-x
Jones, CB, Karin, T, Jain, A, Hobbs, WB, & … (2019). Geographic assessment of photovoltaic module environmental degradation stressors. 2019 IEEE 46th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8980741/
Jones, CB, & Hansen, CW (2019). Single diode parameter extraction from in-field photovoltaic IV curves on a single board computer. 2019 IEEE 46th Photovoltaic …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8981330/
Camargo, LR, Valdes, J, Macia, YM, & Dorner, W (2019). Assessment of on-site steady electricity generation from renewable energy sources in Chile. Energy Procedia, Elsevier, https://www.sciencedirect.com/science/article/pii/S1876610219302772
Skomedal, Å, Øgaard, MB, Selj, J, Haug, H, & … (2019). General, robust, and scalable methods for string level monitoring in utility scale PV systems. 36th European …, researchgate.net, https://www.researchgate.net/profile/Asmund_Skomedal2/publication/337113457_General_Robust_and_Scalable_Methods_for_String_Level_Monitoring_in_Utility_Scale_PV_Systems/links/5dc5abdf4585151435f7da0c/General-Robust-and-Scalable-Methods-for-String-Level-Monitoring-in-Utility-Scale-PV-Systems.pdf
Lorenz, E, Holland, N, Dittmann, A, Herzberg, W, & … (2019). Minute resolution measurement network for global horizontal and tilted solar irradiance for a transmission System Control Area in Southern Germany. …, ise.fraunhofer.de, https://www.ise.fraunhofer.de/content/dam/ise/de/documents/publications/conference-paper/37th-eupvsec-2020/Lorenz_5BO74.pdf
Brown, PR, & O’Sullivan, FM (2019). Shaping photovoltaic array output to align with changing wholesale electricity price profiles. Applied Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0306261919314217
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Ascencio-Vásquez, J, Brecl, K, & Topič, M (2019). Methodology of Köppen-Geiger-Photovoltaic climate classification and implications to worldwide mapping of PV system performance. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X19308527
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Alatawi, H (2019). Determining an Optimal Level of Power System Investments Under Large Scale Penetration of Solar Power in Saudi Arabia., diva-portal.org, https://www.diva-portal.org/smash/get/diva2:1414138/FULLTEXT01.pdf
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Kam, M Van Der, Peters, A, Sark, W Van, & Alkemade, F (2019). Agent-based modelling of charging behaviour of electric vehicle drivers. JASSS, dspace.library.uu.nl, https://dspace.library.uu.nl/handle/1874/387983
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Vrettos, E, Kara, EC, Stewart, EM, & … (2019). Estimating PV power from aggregate power measurements within the distribution grid. Journal of Renewable …, aip.scitation.org, https://doi.org/10.1063/1.5094161
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Zapata, MZ, Wu, E, & Kleissl, J (2019). Irradiance enhancement events in the coastal Stratocumulus dissipation process. Santiago, proceedings.ises.org, http://proceedings.ises.org/paper/swc2019/swc2019-0199-ZamoraZapata.pdf
Gašparović, I, Gašparović, M, Medak, D, & Zrinjski, M (2019). Analysis of Solar Potential Spatial Data for Croatia. Geodetski list, hrcak.srce.hr, https://hrcak.srce.hr/clanak/319451
Carpentier, P, Chancelier, JP, Lara, M De, & Rigaut, T (2019). Algorithms for two-time scales stochastic optimization with applications to long term management of energy storage., hal.archives-ouvertes.fr, https://hal.archives-ouvertes.fr/hal-02013969/
Hertsberg, A (2019). A statistical approach for assessing cloud conditions using photovoltaic solar electricity production data., aaltodoc.aalto.fi, https://aaltodoc.aalto.fi/bitstream/handle/123456789/41704/master_Hertsberg_Axel_2019.pdf?sequence=1
Gašparović, I, Gašparović, M, Medak, D, & … (2019). Analiza prostornih podataka o solarnom potencijalu za Hrvatsku. Geodetski list …, researchgate.net, https://www.researchgate.net/profile/Mateo-Gasparovic/publication/332415521_Analysis_of_Solar_Potential_Spatial_Data_for_Croatia/links/5cb4670792851c8d22ec533e/Analysis-of-Solar-Potential-Spatial-Data-for-Croatia.pdf
Souza, BM (2019). Algoritmo de Projeto e Gestão de Subsistema de Alimentação Baseado em Energia Solar para Redes de Sensores Sem Fios., recipp.ipp.pt, https://recipp.ipp.pt/handle/10400.22/15778
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Leedy, AW, & Abdelraziq, M (2019). Modeling PV Modules Using Simulink/MATLAB under Varying Conditions. 2019 SoutheastCon, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9020537/
Bird, J (2019). Atmospherically Aware Aircraft Guidance Using In Situ Observations., search.proquest.com, https://search.proquest.com/openview/19bbb87b2d8568b57e0b5655fa3aaf96/1?pq-origsite=gscholar&cbl=44156
Mathe, J, Miolane, N, Sebastien, N, & Lequeux, J (2019). PVNet: A LRCN architecture for spatio-temporal photovoltaic PowerForecasting from numerical weather prediction. arXiv preprint arXiv …, arxiv.org, https://arxiv.org/abs/1902.01453
Meunier, S, Heinrich, M, Quéval, L, Cherni, JA, Vido, L, & … (2019). A validated model of a photovoltaic water pumping system for off-grid rural communities. Applied Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0306261919304349
Jordan, DC, Deline, C, Deceglie, MG, & … (2019). Reducing interanalyst variability in photovoltaic degradation rate assessments. IEEE Journal of …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8876837/
Bidikar, N, Rajitha, K, & Supriya, PU (2019). A Novel Universal Solar Energy Predictor. arXiv preprint arXiv:1902.06660, arxiv.org, https://arxiv.org/abs/1902.06660
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Suk, H, & Hall, J (2019). Integrating quality of life in sociotechnical design: a review of microgrid design tools and social indicators. International Design Engineering …, asmedigitalcollection.asme.org, https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings-abstract/IDETC-CIE2019/V02BT03A047/1069848
Brady, J, Chu, C, Colter, E, & … (2019). Methodology for Climate Central’s WeatherPowerTM (Wind and Solar Electricity Forecaster), version 2.0. Climate …, wp-static-prod.s3.amazonaws.com, http://wp-static-prod.s3.amazonaws.com/WeatherPower_v2.0_Methodology_20Sep2019.pdf
Bruck, A (2019). Artificial Intelligence in rural offgrid Polygeneration Systems:: A Case Study with RVE. Sol focusing on Electricity Supply & Demand Balancing., diva-portal.org, https://www.diva-portal.org/smash/record.jsf?pid=diva2:1372646
Fonseca, JA, Hanif, S, Sreepathi, BK, Troitzsch, S, & … (2019). Connecting District Energy and Power Systems for Future Singaporean New Towns (CONCEPT)., research-collection.ethz.ch, https://www.research-collection.ethz.ch/handle/20.500.11850/401217
Prinsloo, FC (2019). Development of a GIS-based decision support tool for environmental impact assessment and due-diligence analyses of planned agricultural floating solar …. University of South Africa, agrisolarclearinghouse.org, https://www.agrisolarclearinghouse.org/wp-content/uploads/2021/11/DEVELOPMENT-OF-A-GIS-BASED-DECISION-SUPPORT.pdf
Gong, J (2019). Optimisation of charging strategies and energy storage operation for a solar driven charging station., diva-portal.org, https://www.diva-portal.org/smash/record.jsf?pid=diva2:1423614
Memmel, E, Peters, D, Völker, R, Schuldt, F, & … (2019). Simulation of vertical power flow at MV/HV transformers for quantification of curtailed renewable power. IET Renewable, https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/iet-rpg.2019.0218.
Schniewind, BSM (2019). Tackling challenges in a decentralised multi-agent smart grid with biomimetic stability mechanisms and unsupervised control–A first approach., researchgate.net, https://www.researchgate.net/profile/Matthias-Schniewind-2/publication/332029214_Tackling_challenges_in_a_decentralised_multi-agent_smart_grid_with_biomimetic_stability_mechanisms_and_unsupervised_control_-_A_first_approach/links/5c9b91c6a6fdccd4603f15f8/Tackling-challenges-in-a-decentralised-multi-agent-smart-grid-with-biomimetic-stability-mechanisms-and-unsupervised-control-A-first-approach.pdf
Peet, BJA (2019). Accurate estimation of temperature distributions for IR signature monitoring with a dynamic thermal model and data assimilation. Target and Background Signatures V, spiedigitallibrary.org
Bird, JJ, Richardson, SJ, & Langelaan, JW (2019). Estimating the vertical structure of weather-induced mission costs for small UAS. Sensors, mdpi.com, https://www.mdpi.com/482974
Baumann, VAR (2019). Viabilidade econômico-financeira de sistemas fotovoltaicos conectados à rede em Florianópolis-SC utilizando dados monitorados e simulados do primeiro ano., repositorio.ifsc.edu.br, https://repositorio.ifsc.edu.br/bitstream/handle/123456789/1281/TCC%20-%20Victor%20A%20R%20Baumann.pdf?sequence=1
Bloch, L, Holweger, J, Ballif, C, & Wyrsch, N (2019). Impact of advanced electricity tariff structures on the optimal design, operation and profitability of a grid-connected PV system with energy storage. Energy Informatics, Springer, https://doi.org/10.1186/s42162-019-0085-z
Qi, B (2019). Learning to Control Home Batteries in the Smart Grid., era.library.ualberta.ca, https://era.library.ualberta.ca/items/58d6cb74-6652-4c18-be88-ff83630d10d9
Toledo, C, Serrano-Lujan, L, Abad, J, Lampitelli, A, & … (2019). Measurement of thermal and electrical parameters in photovoltaic systems for predictive and cross-correlated monitorization. Energies, mdpi.com, https://www.mdpi.com/414352
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Brolese, RR (2019). Previsão de geração de energia fotovoltaica utilizando método de aprendizado de máquina., repositorio.ucs.br, https://repositorio.ucs.br/xmlui/handle/11338/6132
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Less, B, Walker, I, Slack, J, Rainer, L, & Levinson, R (2019). Sealed and Insulated Attic Hygrothermal Performance in New California Homes Using Vapor and Air Permeable Insulation—Field Study and Simulation., escholarship.org, https://escholarship.org/uc/item/5m0052v8
Arias, CA Toledo, Luján, L Serrano, López, J Abad, & … (2019). Measurement of thermal and electrical parameters in photovoltaic systems for predictive and cross-correlated monitorization., repositorio.upct.es, https://repositorio.upct.es/handle/10317/9710
French, R, Braid, JL, & Liu, JQ (2019). Module Level Exposure and Evaluation Test (MLEET) for Real-world and Laboratory-based PV Modules: Common Data and Analytics for Quantitative Cross …., osti.gov, https://www.osti.gov/biblio/1529093
Stainsby, WJ (2019). Disaggregation of net-metered advanced metering infrastructure data to estimate photovoltaic generation., search.proquest.com, https://search.proquest.com/openview/978297252d3c3d422e52c61bf3d64328/1?pq-origsite=gscholar&cbl=51922&diss=y
Li, D (2019). Micro optics for micro hybrid concentrator photovoltaics., dspace.mit.edu, https://dspace.mit.edu/handle/1721.1/123563
Edington, ANC (2019). The role of long duration energy storage in decarbonizing power systems., dspace.mit.edu, https://dspace.mit.edu/handle/1721.1/122095
Gonzalez-Cruz, JE, Arend, M, Legbandt, T, & … (2019). Method for forecasting energy demands that incorporates urban heat island. US Patent App. 16 …, Google Patents, https://patents.google.com/patent/US20190139163A1/en
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Pujol, R Colom (2019). Estudi de diferents opcions d’integració de generació fotovoltaica en edificis., upcommons.upc.edu, https://upcommons.upc.edu/handle/2117/128705
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2018

Holmgren, WF, Hansen, CW, & … (2018). pvlib python: A python package for modeling solar energy systems. Journal of Open Source …, joss.theoj.org, https://doi.org/10.21105/joss.00884
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Holmgren, WF, Hansen, CW, Stein, JS, & … (2018). Review of open source tools for PV modeling. 2018 IEEE 7th World …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8548231/
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Ellis, BH, Deceglie, M, & Jain, A (2018). Automatic detection of clear-sky periods using ground and satellite based solar resource data. … )(A Joint Conference of 45th IEEE …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8547877/
김태영, & 김진호 (2018). 서포트 벡터 회귀와 파이썬 라이브러리를 활용한 태양광 발전량 예측. 대한전기학회학술대회논문집, dbpia.co.kr, https://www.dbpia.co.kr/Journal/articleDetail?nodeId=NODE07532390
Stein, JS, & Lavrova, O (2018). Final Project Report: PV Stakeholder Engagement Initiatives., osti.gov, https://www.osti.gov/biblio/1481543
Klise, KA (2018). Pecos-Open Source Software for PV System Monitoring.., osti.gov, https://www.osti.gov/servlets/purl/1525601
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John, JJ, Alnuaimi, A, Elnosh, A, & … (2018). Estimating degradation rates from 27 different PV modules installed in desert conditions using the NREL/Rdtools. 2018 IEEE 7th World …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8547283/
Potter, BG, Simmons-Potter, K, & … (2018). Broad-Time-Horizon Solar Power Prediction and PV Performance Degradation Research at the University of Arizona. 2018 IEEE 7th World …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8547422/
Berrueta, A, Heck, M, Jantsch, M, Ursúa, A, & Sanchis, P (2018). Combined dynamic programming and region-elimination technique algorithm for optimal sizing and management of lithium-ion batteries for photovoltaic …. Applied energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0306261918309279
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Luo, W, Khoo, YS, Hacke, P, Jordan, D, & … (2018). Analysis of the long-term performance degradation of crystalline silicon photovoltaic modules in tropical climates. IEEE Journal of …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8520911/
Bhattacharya, S, Leopold, U, Braun, C, & Bongiovanni, F (2018). Solar Energy Potential Assessment for entire Cities-a reusable and scalable approach., scholarsarchive.byu.edu, https://scholarsarchive.byu.edu/iemssconference/2018/Stream-A/62/
Litjens, G, Worrell, E, & Sark, W Van (2018). Assessment of forecasting methods on performance of photovoltaic-battery systems. Applied Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0306261918305014
Luján, E, Otero, A, Valenzuela, S, Mocskos, E, & … (2018). Cloud computing for smart energy management (CC-SEM project). … -American Congress of …, Springer, https://doi.org/10.1007/978-3-030-12804-3_10
Rognan, LMH (2018). Photovoltaic Power Prediction and Control Strategies of the Local Storage Unit at Campus Evenstad., ntnuopen.ntnu.no, https://ntnuopen.ntnu.no/ntnu-xmlui/handle/11250/2570005
Yang, D (2018). SolarData: An R package for easy access of publicly available solar datasets. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X18306583
Camargo, LR, Nitsch, F, Gruber, K, & Dorner, W (2018). Electricity self-sufficiency of single-family houses in Germany and the Czech Republic. Applied energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0306261918309875
Alonso-Álvarez, D, Wilson, T, Pearce, P, Führer, M, & … (2018). Solcore: a multi-scale, Python-based library for modelling solar cells and semiconductor materials. Journal of …, Springer, https://doi.org/10.1007/s10825-018-1171-3
Craig, MT, Jaramillo, P, Hodge, BM, Williams, NJ, & … (2018). A retrospective analysis of the market price response to distributed photovoltaic generation in California. Energy policy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0301421518303781
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Bognár, Á, Loonen, R, Valckenborg, RME, & Hensen, JLM (2018). An unsupervised method for identifying local PV shading based on AC power and regional irradiance data. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X18309873
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Kumler, A, Xie, Y, & Zhang, Y (2018). A new approach for short-term solar radiation forecasting using the estimation of cloud fraction and cloud albedo., osti.gov, https://www.osti.gov/biblio/1476449
Khosrojerdi, F, Gagnon, S, & Valverde, R (2018). Using Software Quality Evaluation Standard Model for Managing Software Development Projects in Solar Sector., spectrum.library.concordia.ca, https://spectrum.library.concordia.ca/id/eprint/984517/
Litjens, GBMA, Kausika, BB, Worrell, E, & Sark, W van (2018). A spatio-temporal city-scale assessment of residential photovoltaic power integration scenarios. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X18309496
Sun, X, Khan, MR, Deline, C, & Alam, MA (2018). Optimization and performance of bifacial solar modules: A global perspective. Applied energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0306261917317567
Hobbs, WB, Lave, M, & … (2018). Simulating High-Frequency Generation Profiles for Large Solar PV Portfolios. 2018 IEEE 7th World …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8547850/
Pelaez, SA, Deline, CA, Greenberg, P, Stein, J, & Kostuk, R (2018). Model and Validation of Single-Axis Tracking with Bifacial Photovoltaics., osti.gov, https://www.osti.gov/biblio/1478728
Riihelä, A, Kallio, V, Devraj, S, Sharma, A, & Lindfors, AV (2018). Validation of the SARAH-E satellite-based surface solar radiation estimates over India. Remote Sensing, mdpi.com, https://www.mdpi.com/269038
Zhao, B, Sun, X, & Alam, MA (2018). Online Simulation Tools for Global Photovoltaic Performance: Purdue University Meteorological Tool (PUMET) and Bifacial Module Calculator (PUB). 2018 IEEE 7th World Conference on …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8547981/
Nazarian, N, Sin, T, & Norford, L (2018). Numerical modeling of outdoor thermal comfort in 3D. Urban climate, Elsevier, https://www.sciencedirect.com/science/article/pii/S2212095518300786
Nunnari, G (2018). Forecasting the Class of Daily Clearness Index for PV Applications.. ICINCO (1), pdfs.semanticscholar.org, https://pdfs.semanticscholar.org/ed6a/8901b1adf3e6d15c3747968e718e1f3eacdd.pdf
Prada, J, & Dorronsoro, JR (2018). General noise support vector regression with non-constant uncertainty intervals for solar radiation prediction. … of Modern Power Systems and Clean …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9024331/
Gašparović, I, Gašparović, M, & Medak, D (2018). Determining and analysing solar irradiation based on freely available data: A case study from Croatia. Environmental Development, Elsevier, https://www.sciencedirect.com/science/article/pii/S2211464517302105
Boudon, F, Persello, S, Grechi, I, Marquier, A, & … (2018). Assessing the role of ageing and light availability in leaf mortality in the mango tree. … Systems of 1281, actahort.org, https://www.actahort.org/books/1281/1281_79.htm
Duan, J, McKenna, A, Kooten, GC Van, & Liu, S (2018). Renewable Electricity Grids, Battery Storage and Missing Money: An Alberta Case Study., ageconsearch.umn.edu, https://ageconsearch.umn.edu/record/277525/
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Yang, J, Li, X, Peng, W, Wagner, F, & … (2018). Climate, air quality and human health benefits of various solar photovoltaic deployment scenarios in China in 2030. Environmental …, iopscience.iop.org, https://doi.org/10.1088/1748-9326/aabe99
Lindig, S, Ingenhoven, PC, Belluardo, G, & … (2018). Evaluation of Technology-Dependent Maximum Power Point Current and Voltage Degradation in a Temperate Climate. EUPVSEC European PV …, bia.unibz.it, https://bia.unibz.it/esploro/outputs/conferenceProceeding/Evaluation-of-Technology-Dependent-Maximum-Power-Point-Current-and-Voltage-Degradation-in-a-Temperate-Climate/991005773038401241
Tzikas, C, Valckenborg, RME, & … (2018). Outdoor characterization of colored and textured prototype PV facade elements. 35th European …, researchgate.net, https://www.researchgate.net/profile/Chris-Tzikas/publication/330349994_Outdoor_Characterization_of_Colored_and_Textured_Prototype_PV_Facade_Elements/links/5c3b0d62458515a4c7225c4d/Outdoor-Characterization-of-Colored-and-Textured-Prototype-PV-Facade-Elements.pdf
Scolari, E, Sossan, F, Haure-Touzé, M, & Paolone, M (2018). Local estimation of the global horizontal irradiance using an all-sky camera. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X18308119
Nascimento, MMS (2018). Optimization of memory usage in quantum computing simulation., repositorio.ufpel.edu.br, http://repositorio.ufpel.edu.br:8080/handle/prefix/4355
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Raker, D, Kini, R, Huntsman, R, Green, M, & … (2018). Transactive Mitigation Of Variability In The Output Of 1 MW Photovoltaic Array Using VolttronTM. 2018 IEEE 7th World …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8548242/
Gurupira, TL (2018). Evaluation and optimisation of photovoltaic (PV) plant designs., scholar.sun.ac.za, http://scholar.sun.ac.za/handle/10019.1/103499
Woodruff, DL, Deride, J, Staid, A, Watson, JP, Slevogt, G, & … (2018). Constructing probabilistic scenarios for wide-area solar power generation. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X17310605
Peronato, G, Rastogi, P, Rey, E, & Andersen, M (2018). A toolkit for multi-scale mapping of the solar energy-generation potential of buildings in urban environments under uncertainty. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X18307862
Mejia, JF, Giordano, M, & Wilcox, E (2018). Conditional summertime day-ahead solar irradiance forecast. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X18301154
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Spiess, R (2018). Imitation Learning for Photovoltaic Power Fluctuation., research-collection.ethz.ch, https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/303532/1/Spiess_Robin.pdf
Ruth, D, & Muller, M (2018). A methodology to analyze photovoltaic tracker uptime. Progress in Photovoltaics: Research and …, Wiley Online Library, https://doi.org/10.1002/pip.3002
Lago, J, Brabandere, K De, Ridder, F De, & … (2018). A generalized model for short-term forecasting of solar irradiance. … IEEE Conference on …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8618693/
Hafez, AAA, & Alblawi, A (2018). A feasibility study of PV installation: Case study at Shaqra University. 2018 9th International Renewable …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8362486/
Litjens, G, Worrell, E, & Sark, W Van (2018). Economic benefits of combining self-consumption enhancement with frequency restoration reserves provision by photovoltaic-battery systems. Applied energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0306261918305622
Bognár, Á, Loonen, R, Valckenborg, RME, & … (2018). Modeling reflected irradiance in urban environments–a case study for simulation-based measurement quality control for an outdoor PV test site. … Energy Conference and …, pure.tue.nl, https://pure.tue.nl/ws/files/110724078/6DO.10.4_paper.pdf
Bruinewoud, M (2018). Assessing the performance of the Utrecht Science Park PV system.
Moraitis, P, Kausika, BB, Nortier, N, & Sark, W Van (2018). Urban environment and solar PV performance: the case of the Netherlands. Energies, mdpi.com, https://www.mdpi.com/297244
Klise, KA, & Hart, D (2018). Pecos and Canary Webinar.., osti.gov, https://www.osti.gov/servlets/purl/1511107
Taghezouit, B, Arab, A Hadj, Larbes, C, & … (2018). Dynamic Modelling and Performance Analysis for a Grid-Connected PV System under LabVIEW. … Seminar on New and …, researchgate.net, https://www.researchgate.net/profile/A-Hadj-Arab/publication/328601814_Dynamic_Modelling_and_Performance_Analysis_for_a_Grid-Connected_PV_System_under_LabVIEW/links/610931a9169a1a0103d4fa4f/Dynamic-Modelling-and-Performance-Analysis-for-a-Grid-Connected-PV-System-under-LabVIEW.pdf
Massidda, L, & Marrocu, M (2018). Quantile regression post-processing of weather forecast for short-term solar power probabilistic forecasting. Energies, mdpi.com, https://www.mdpi.com/312484
Dzurick, MR (2018). Exploration of Sensor Systems for Increasing the Accuracy of Photovoltaic Modeling Application to the TEP/AzRISE Solar Test Yard., search.proquest.com, https://search.proquest.com/openview/6c20a34c4344792b63248cd4e46a4d08/1?pq-origsite=gscholar&cbl=18750&diss=y
Kam, MJ Van der, Meelen, AAH, Sark, W Van, & … (2018). Diffusion of solar photovoltaic systems and electric vehicles among Dutch consumers: Implications for the energy transition. Energy research & …, Elsevier, https://www.sciencedirect.com/science/article/pii/S2214629618305875
MacAlpine, SM, Wolfrom, CW, & … (2018). Evaluation of Irradiance Transposition Models When Utilized with Single Axis Tracking PV Systems in the Southwestern United States. 2018 IEEE 7th World …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8547777/
Hariharan, A, Karady, GG, & … (2018). Application of Machine Learning Algorithm to Forecast Load and Development of a Battery Control Algorithm to Optimize PV System Performance in Phoenix, Arizona. 2018 North American …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8600594/
Buffat, R, Grassi, S, & Raubal, M (2018). A scalable method for estimating rooftop solar irradiation potential over large regions. Applied energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0306261918301272
DiOrio, NA, Blair, NJ, Freeman, JM, Habte, AM, & … (2018). Solar System Modeling at NREL., osti.gov, https://www.osti.gov/biblio/1477226
Lago, J, Brabandere, K De, Ridder, F De, & Schutter, B De (2018). Short-term forecasting of solar irradiance without local telemetry: A generalized model using satellite data. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X18307138
Øgaard, MB, Haug, H, & Selj, JHK (2018). Methods for quality control of monitoring data from commercial PV systems. Proceedings of the European …, duo.uio.no, https://www.duo.uio.no/handle/10852/71679
Dupin, N (2018). Portfolio balancing strategy for the integration of renewable energy sources to the day ahead market., diva-portal.org, https://www.diva-portal.org/smash/record.jsf?pid=diva2:1295919
Schweighofer, B, Buchroithner, A, & … (2018). Generic Simulation Framework for Grid-Connected Photovoltaic and Energy Storage Systems. 2018 IEEE Green …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8745548/
Narayan, N, Vega-Garita, V, Qin, Z, & … (2018). A modeling methodology to evaluate the impact of temperature on Solar Home Systems for rural electrification. 2018 IEEE …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8398756/
Stein, J (2018). FY2018 PV Lifetime Annual Report.., osti.gov, https://www.osti.gov/servlets/purl/1484335
Li, X (2018). Radiative Effects of Atmospheric Aerosols and Impacts on Solar Photovoltaic Electricity Generation., search.proquest.com, https://search.proquest.com/openview/e669cd1478b33d5212c565885a26944c/1?pq-origsite=gscholar&cbl=18750
Prisikar, M (2018). Feasibility study of renovation of a residential building in near zero-energy building., aaltodoc.aalto.fi, https://aaltodoc.aalto.fi/handle/123456789/32460
Beebe, NHF (2018). A Complete Bibliography of Publications in the Journal of Open Source Software., netlib.org, http://www.netlib.org/tex/bib/joss.pdf
Prieto, E Castillo (2018). Predicción de Energía Fotovoltaica a partir de Ensembles NWP., repositorio.uam.es, https://repositorio.uam.es/handle/10486/685414
Mountain, B, Percy, S, Kars, A, Saddler, H, & Billimoria, F (2018). Does renewable electricity generation reduce electricity prices?., apo.org.au, https://apo.org.au/node/226981
Litjens, G (2018). Here comes the sun: Improving local use of electricity generated by rooftop photovoltaic systems., dspace.library.uu.nl, https://dspace.library.uu.nl/handle/1874/373082
Edition, F (2018). A Solar Design Manual for Alaska., acep.uaf.edu, https://acep.uaf.edu/media/260463/EEM-01255_SolarDesignManual_5thEd201805.pdf
Nagel, J (2018). Optimization of Energy Supply Systems., Springer, https://doi.org/10.1007/978-3-319-96355-6
Touz, N Le (2018). Conception et étude d’infrastructures de transports à énergie positive: de la modélisation thermomécanique à l’optimisation de tels systèmes énergétiques., tel.archives-ouvertes.fr, https://tel.archives-ouvertes.fr/tel-01959310/
Agutu, CO (2018). Influence of spectral beam splitting on the performance of polycrystalline silicon PV cells., repository.up.ac.za, https://repository.up.ac.za/handle/2263/66384
Krishnamurthy, CKB, Vesterberg, M, Böök, H, & … (2018). Real-time pricing revisited: Demand flexibility in the presence of micro-generation. Energy policy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0301421518305524
Wilson, TA (2018). Evaluating the Effectiveness of Current Atmospheric Refraction Models in Predicting Sunrise and Sunset Times., search.proquest.com, https://search.proquest.com/openview/22ac065f7c59d07245c502279b2849eb/1?pq-origsite=gscholar&cbl=18750
Huuki, H, Karhinen, S, Böök, H, Lindfors, AV, & … (2018). Virtual Power Plant Operation with Solar Power Forecast Errors and Demand Response. Available at SSRN …, papers.ssrn.com, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3277354
Yang, D, Kleissl, J, Gueymard, CA, Pedro, HTC, & … (2018). History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X17310022
Krishnamurthy, CK, Vesterberg, M, Böök, H, Lindfors, AV, & … (2018). Benefits of real-time pricing and rooftop solar PV generation: Explorations using Swedish micro-data., papers.ssrn.com, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3144215
Jacobo, AF Zambrano (2018). Predicción de irradiación solar en Colombia a partir de mediciones en sitio y satelitales utilizando redes neuronales., repositorio.uniandes.edu.co, https://repositorio.uniandes.edu.co/bitstream/handle/1992/39107/u820956.pdf?sequence=1
Han, X, & Xu, L (2018). Technology Adoption in Input-Output Networks. Available at SSRN 3266251, papers.ssrn.com, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3266251
Freitas, SRT (2018). Photovoltaic potential in building façades., repositorio.ul.pt, https://repositorio.ul.pt/handle/10451/34868
Sartor, K (2018). Développement d’un outil de simulation et d’analyse technico-économique et environnementale d’un réseau de chaleur., orbi.uliege.be, https://orbi.uliege.be/handle/2268/229271
Scheiber, G (2018). Gesamtheitliche Modellierung von leitungsgebundenen Energiesystemen mit exergetischer Bewertung., pure.unileoben.ac.at, https://pure.unileoben.ac.at/portal/en/publications/gesamtheitliche-modellierung-von-leitungsgebundenen-energiesystemen-mit-exergetischer-bewertung(ee724293-b5e1-4547-bde4-0add5f1641fd).html
Gurupira, T, & Rix, A (2017). Pv simulation software comparisons: Pvsyst, nrel sam and pvlib. Conf.: SAUPEC, orcun.baslak.com.

2017

Holmgren, WF, Lorenzo, AT, & … (2017). A comparison of pv power forecasts using pvlib-python. 2017 IEEE 44th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8366724/
Stein, J (2017). PV Performance Modeling Methods and Practices.., osti.gov, https://www.osti.gov/servlets/purl/1474242
Stein, J (2017). Challenges of PV Degradation Analysis: PVLIB and Performance Data Analysis.., osti.gov, https://www.osti.gov/servlets/purl/1482294
Ransome, S. and Sutterlueti, J. (2017). “OPTIMUM USE OF THE LOSS FACTORS MODEL (LFM) FOR IMPROVED PV PERFORMANCE MODELLING”, 13th PVSAT 2017 Bangor, UK,
Ransome, S. and Sutterlueti, J. (2017). “Choosing the best Empirical Model for predicting energy yield”, 7th PV Energy Rating and Module Performance Modeling Workshop, Canobbio, Switzerland 2017, https://www.slideshare.net/sandiaecis/15-2017-pvpmc7ransome170330t081corrected2-74980695
Gurupira, T, & Rix, AJ (2017). Pv simulation software comparisons: Pvsyst, nrel sam and pvlib,(january).
Benito, AJ Gil de Santivañes de (2017). Interfaz gráfica para el modelado de sistemas fotovoltaicos mediante el paquete de funciones de código abierto PVLIB.., oa.upm.es, https://oa.upm.es/id/eprint/47456
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Li, X, Wagner, F, Peng, W, Yang, J, & … (2017). Reduction of solar photovoltaic resources due to air pollution in China. Proceedings of the …, National Acad Sciences, https://www.pnas.org/content/114/45/11867.short
Mikofski, MM, Hansen, CW, & … (2017). Use of measured aerosol optical depth and precipitable water to model clear sky irradiance. 2017 IEEE 44th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8366314/
Klise, KA, Stein, JS, & … (2017). Application of IEC 61724 Standards to Analyze PV System Performance in Different Climates. 2017 IEEE 44th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8366666/
Hansen, CW, Stein, J, & Riley, DM (2017). 2017 IEEE PVSC tutorial PV system modeling.., osti.gov, https://www.osti.gov/servlets/purl/1513607
Gurupira, T, & Rix, A (2017). PV simulation software comparisons: PVSYST. NREL SAM AND PVLIB
Anoma, M Abou, Jacob, D, Bourne, BC, & … (2017). View factor model and validation for bifacial PV and diffuse shade on single-axis trackers. 2017 IEEE 44th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8366704/
Klise, GT (2017). Reliability Impacts to PV Plant Performance: Methods and Tools for O&M Insight.., osti.gov, https://www.osti.gov/servlets/purl/1427416
Jordan, DC, Deline, C, Kurtz, SR, & … (2017). Robust PV degradation methodology and application. IEEE Journal of …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8233204/
Lee, KH, Araki, K, Elleuch, O, Kojima, N, & … (2017). Pypvcell: An open-source solar cell modeling library in Python. 2017 IEEE 44th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8366371/
Klise, KA (2017). Pecos Open Source Software for PV Performance Monitoring.., osti.gov, https://www.osti.gov/servlets/purl/1457957
Kühnel, M, Hanke, B, Geißendörfer, S, & … (2017). Energy forecast for mobile photovoltaic systems with focus on trucks for cooling applications. Progress in …, Wiley Online Library, https://doi.org/10.1002/pip.2886
Haapaniemi, J, Narayanan, A, Tikka, V, & … (2017). Effects of major tariff changes by distribution system operators on profitability of photovoltaic systems. … Conference on the …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/7981935/
Petric, T, Dupont, C, & Gall, F Le (2017). Evaluating benefits of adding intelligence to small-scale renewable energy systems. IEEE EUROCON 2017-17th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8011139/
Stein, JS, Riley, D, Lave, M, Hansen, C, & … (2017). Outdoor field performance from bifacial photovoltaic modules and systems. 2017 IEEE 44th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8366042/
Litjens, G, Kausika, BB, Worrell, E, & … (2017). Spatial Analysis of Residential Combined Photovoltaic and Battery Potential: Case Study Utrecht, the Netherlands. 2017 IEEE 44th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8366519/
II, DJ Gagne, McGovern, A, Haupt, SE, & Williams, JK (2017). Evaluation of statistical learning configurations for gridded solar irradiance forecasting. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X17303158
Klise, GT, Freeman, JM, & Lavrova, O (2017). Simulating PV System Performance with Component Reliability Distributions. 2017 IEEE 44th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8366397/
Litjens, G, Worrell, E, & Sark, W Van (2017). Influence of demand patterns on the optimal orientation of photovoltaic systems. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X17305856
Catalina, A, Torres-Barrán, A, & Dorronsoro, JR (2017). Satellite based nowcasting of PV energy over peninsular Spain. … Work-Conference on …, Springer, https://doi.org/10.1007/978-3-319-59153-7_59
Haschke, J, Seif, JP, Riesen, Y, Tomasi, A, & … (2017). Energy Yield in Hot & Sunny Climates: Impact of Silicon Solar Cell Architecture and Cell Interconnection. 2017 IEEE 44th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8366703/
Chen, D, & Irwin, D (2017). Black-box solar performance modeling: Comparing physical, machine learning, and hybrid approaches. ACM SIGMETRICS Performance Evaluation Review, dl.acm.org, https://doi.org/10.1145/3152042.3152067
Louwen, A, Schropp, REI, Sark, WG van, & Faaij, APC (2017). Geospatial analysis of the energy yield and environmental footprint of different photovoltaic module technologies. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X17306412
Elsinga, B, Sark, W van, & … (2017). Inverse photovoltaic yield model for global horizontal irradiance reconstruction. Energy Science & …, Wiley Online Library, https://doi.org/10.1002/ese3.162
Pannebakker, BB, Waal, AC de, & … (2017). Photovoltaics in the shade: one bypass diode per solar cell revisited. Progress in …, Wiley Online Library, https://doi.org/10.1002/pip.2898
Moraitis, P, Kausika, BB, & … (2017). Effects of Urban Environment on Solar PV Performance. 2017 IEEE 44th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8366669/
Jenson, D, D’Sa, R, Henderson, T, Kilian, J, & … (2017). Energy characterization of a transformable solar-powered unmanned aerial vehicle. 2017 IEEE/RSJ …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8206401/
Computação, C da (2017). Mateus MS do Nascimento.
Polo, J, Fernandez-Neira, WG, & Alonso-García, MC (2017). On the use of reference modules as irradiance sensor for monitoring and modelling rooftop PV systems. Renewable energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0960148117300265
Steiner, M, Gerstmaier, T, & Bett, AW (2017). Concentrating photovoltaic systems. The Performance of Photovoltaic (PV) …, Elsevier, https://www.sciencedirect.com/science/article/pii/B9781782423362000100
Curran, AJ, Hu, Y, Haddadian, R, Braid, JL, & … (2017). Determining the power rate of change of 353 plant inverters time-series data across multiple climate zones, using a month-by-month data science analysis. 2017 IEEE 44th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8366477/
Hamann, HF (2017). A multi-scale, multi-model, machine-learning solar forecasting technology., osti.gov, https://www.osti.gov/biblio/1395344
Thiébaut, J (2017). Theoretical and experimental investigations of parabolic trough collectors for a small-scale solar thermal power plant., matheo.uliege.be, https://matheo.uliege.be/handle/2268.2/3210
López, AB Cristóbal, Nadal, C Cañizo, Vega, A Martí, & … (2017). Fostering a Next GeneRation of European Photovoltaic SoCiety through Open Science-GRECO 787289., oa.upm.es, http://oa.upm.es/50489/1/GRECO_section1-3_abc45%20.pdf
Lovati, M, Maturi, L, Adami, J, & Moser, D (2017). Methodologies and tools for BIPV implementation in the early stage of the architectural design. 12th conference on Advanced …, iris.unitn.it, https://iris.unitn.it/bitstream/11572/263544/4/Tesi_Lovati_202005_definitiva.pdf
Chambers, JD (2017). Developing a rapid, scalable method of thermal characterisation for UK dwellings using smart meter data., discovery.ucl.ac.uk, https://discovery.ucl.ac.uk/id/eprint/10030678/

2016

Stein, JS, Holmgren, WF, Forbess, J, & … (2016). PVLIB: Open source photovoltaic performance modeling functions for Matlab and Python. 2016 ieee 43rd …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/7750303/
Holmgren, WF, & Groenendyk, DG (2016). An open source solar power forecasting tool using PVLIB-Python. 2016 ieee 43rd photovoltaic …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/7749755/
Gurupira, T, & Rix, AJ (2016). Photovoltaic System Modelling using PVLib-Python. Fourth South African Solar Energy …, researchgate.net, https://www.researchgate.net/profile/Arnold-Rix/publication/313249264_PHOTOVOLTAIC_SYSTEM_MODELLING_USING_PVLIB-PYTHON/links/589440ddaca27231daf6340f/PHOTOVOLTAIC-SYSTEM-MODELLING-USING-PVLIB-PYTHON.pdf
Stein, J (2016). 2016 PVLIB Users Group Meeting.., osti.gov, https://www.osti.gov/servlets/purl/1514526
Ransome, S, Stein, J, Holmgren, W, & … (2016). PV Performance modelling with PVPMC/PVLIB. PVSAT12, pdfs.semanticscholar.org, https://pdfs.semanticscholar.org/516e/d000fa1e9910668d6960c05c2db2816e3e27.pdf
Klise, KA, & Stein, JS (2016). Automated performance monitoring for PV systems using pecos. 2016 IEEE 43rd Photovoltaic Specialists …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/7750304/
Li, X, Mauzerall, DL, Wagner, F, & … (2016). Impact of Atmospheric Aerosols on Solar Photovoltaic Electricity Generation in China. AGU Fall Meeting …, ui.adsabs.harvard.edu, https://ui.adsabs.harvard.edu/abs/2016AGUFMGC53G..05L/abstract
Deline, C, DiOrio, N, Jordan, D, & Toor, F (2016). Progress & frontiers in PV performance., osti.gov, https://www.osti.gov/biblio/1327483
Lee, M, & Panchula, A (2016). Spectral correction for photovoltaic module performance based on air mass and precipitable water. 2016 IEEE 43rd Photovoltaic Specialists …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/7749836/
Klise, GT (2016). PV System Reliability: An O&M Perspective.., osti.gov, https://www.osti.gov/servlets/purl/1346103
Passow, K, & Lee, M (2016). Effect of spectral shift on solar PV performance. 2016 IEEE Conference on Technologies for …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/7897175/
Litjens, G, Sark, W Van, & Worrell, E (2016). On the influence of electricity demand patterns, battery storage and PV system design on PV self-consumption and grid interaction. 2016 IEEE 43rd Photovoltaic …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/7749983/
Klise, KA, & Stein, J (2016). Performance Monitoring using Pecos Version 0.1.., osti.gov, https://www.osti.gov/servlets/purl/1734479
Mikofski, M, Oumbe, A, Li, C, & … (2016). Evaluation and correction of the impact of spectral variation of irradiance on pv performance. 2016 ieee 43rd …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/7749837/
Ruf, H, Schroedter-Homscheidt, M, Heilscher, G, & … (2016). Quantifying residential PV feed-in power in low voltage grids based on satellite-derived irradiance data with application to power flow calculations. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X16301803
Polo, J, Garcia-Bouhaben, S, & … (2016). A comparative study of the impact of horizontal-to-tilted solar irradiance conversion in modelling small PV array performance. Journal of Renewable …, aip.scitation.org, https://doi.org/10.1063/1.4964363
Phinikarides, A, Shimitra, C, Bourgeon, R, & … (2016). Development of a novel web application for automatic photovoltaic system performance analysis and fault identification. 2016 IEEE 43rd …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/7749921/
Lassila, J, Tikka, V, Haapaniemi, J, Child, M, Breyer, C, & … (2016). Nationwide photovoltaic hosting capacity in the Finnish electricity distribution system. … Photovoltaic Solar Energy …
Lave, M, Stein, J, & Smith, R (2016). Solar variability datalogger. Journal of Solar …, asmedigitalcollection.asme.org, https://asmedigitalcollection.asme.org/solarenergyengineering/article-abstract/138/5/054503/383682
Ruf, H, Schroedter-Homscheidt, M, Beyer, HG, & … (2016). Simulation of the Load Flow at the Transformer in Low Voltage Distribution Grids with a Significant Number of PV Systems using Satellite-derived Solar …. Sol. Energy, researchgate.net, https://www.researchgate.net/profile/Holger-Ruf/publication/304525176_Simulation_of_the_Load_Flow_at_the_Transformer_in_Low_Voltage_Distribution_Grids_with_a_Significant_Number_of_PV_Systems_using_Satellite-Derived_Solar_Irradiance/links/57723db708ae842225adc8d4/Simulation-of-the-Load-Flow-at-the-Transformer-in-Low-Voltage-Distribution-Grids-with-a-Significant-Number-of-PV-Systems-using-Satellite-Derived-Solar-Irradiance.pdf
Tomažič, T (2016). Napovedovanje dnevne proizvodnje električne energije sončnih elektrarn., eprints.fri.uni-lj.si, http://eprints.fri.uni-lj.si/3650/
Ruf, HI (2016). Computation of the load flow at the transformer in distribution grids with a significant number of photovoltaic systems using satellite-derived solar irradiance data., uia.brage.unit.no, https://uia.brage.unit.no/uia-xmlui/bitstream/handle/11250/2398250/Dissertation_Holger-Ruf_Print-Version_embedded_Fonts.pdf?sequence=1
Hernández-Torres, D, Turpin, C, & … (2016). Modélisation en ﬂux d’énergie d’une batterie Li-Ion en vue d’une optimisation technico économique d’un micro-réseau intelligent. … de Genie Electrique, hal.archives-ouvertes.fr, https://hal.archives-ouvertes.fr/hal-01361618/
Fortuna, L, Nunnari, G, & Nunnari, S (2016). Nonlinear modeling of solar radiation and wind speed time series., Springer, https://doi.org/10.1007/978-3-319-38764-2
Campaigne, C, Balandat, M, & Ratliff, L (2016). Welfare effects of dynamic electricity pricing. Working Paper, ocf.berkeley.edu, https://www.ocf.berkeley.edu/~clay/file/SimulatingDynamicTariffs.pdf
Banadkooki, AS (2016). Prediction of Photovoltaic Power Generation from Cloud Imaging., research-collection.ethz.ch, https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/155770/eth-49449-01.pdf
Altes-Buch, Q (2016). Mechanical design, control and optimization of a hybrid solar microgrid for rural electrification and heat supply in sub-Saharan Africa., orbi.uliege.be, https://orbi.uliege.be/bitstream/2268/208976/1/QAB_MasterThesis.pdf

2015

Holmgren, WF, Andrews, RW, & … (2015). PVLIB python 2015. 2015 ieee 42nd …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/7356005/
Ellis, A (2015). Large-Scale Photovoltaics deployment.., osti.gov, https://www.osti.gov/servlets/purl/1333247
Köhler, C, Ruf, H, Steiner, A, Lee, D, & … (2015). Nutzung Numerischer Wettervorhersagen in der Simulation von Verteilnetzen: Die Effekte einer Sonnenfinsternis auf netzgekoppelte PV-Anlagen und …. 30th Symposium …, researchgate.net, https://www.researchgate.net/profile/Carmen-Koehler/publication/273143752_Nutzung_Numerischer_Wettervorhersagen_in_der_Simulation_von_Verteilnetzen_Die_Effekte_einer_Sonnenfinsternis_auf_netzgekoppelte_PV-Anlagen_und_Netztransformatoren/links/554b7f3f0cf21ed2135948f7/Nutzung-Numerischer-Wettervorhersagen-in-der-Simulation-von-Verteilnetzen-Die-Effekte-einer-Sonnenfinsternis-auf-netzgekoppelte-PV-Anlagen-und-Netztransformatoren.pdf
Ruf, H, Schroedter-Homscheidt, M, & … (2015). Load Flow Calculation of a Low Voltage Transformer using Satellitebased Irradiance Data. … ETG Congress 2015 …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/7388521/
Stein, JS (2015). FY15 Final Technical Report for DOE SunShot., osti.gov, https://www.osti.gov/servlets/purl/1232610
Andrews, RW, Stein, JS, Hansen, C, & … (2014). Introduction to the open source PV LIB for python Photovoltaic system modelling package. 2014 IEEE 40th …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/6925501/
Stein, J. S. and M. Green (2015). Novel strategies for PV system monitoring. PV-Tech Power. London, UK, Solar Media. 02.
Ransome, S., Sutterlueti, J., Scholz, J, Stein, J.S. (2015). Improved PV Performance modelling by combining the PV_LIB Toolbox with the Loss Factors Model (LFM), 42nd IEEE PV Specialists Conference, New Orleans, LA, USA.

2014

Andrews, R. W., J. S. Stein, C. Hansen and D. Riley (2014). Introduction to the open source PV LIB for python Photovoltaic system modelling package. 2014 IEEE 40th photovoltaic specialist conference (PVSC), IEEE.
Riley, DM, Stein, J, Hansen, CW, & Andrews, R (2014). 2014 IEEE PVSC Tutorial on PV System Performance Modeling.., osti.gov, https://www.osti.gov/servlets/purl/1714482
Stein, JS, & Toolbox, P (2014). Sandia National Laboratories: Albuquerque. NM, USA
Rogers, E, & Sexton, S (2014). Distributed Decisions: The Efficiency of Policy for Rooftop Solar Adoption. Online at https://www. aeaweb. org …, evangrogers.org, https://evangrogers.org/wp-content/uploads/2014/04/Rogers_JMP.pdf
Gregg, DC, Murgia, FM, & Seydioglu, B (2014). Solar Panel Layout and Installation. US Patent App. 13/551,863, Google Patents, https://patents.google.com/patent/US20140025343A1/en
Nogueira, PHO (2014). Simulação do desempenho de sistemas solares fotovoltaicos para a geração de eletricidade: um estudo de caso do sistema fotovoltaico da embaixada da Itália., bdm.unb.br, https://bdm.unb.br/handle/10483/9270

2013

Stein, J. S. and B. H. King (2013). Modeling for PV plant optimization. Photovoltaics International, Solar Media Ltd. 19th: 101-109.