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).

Download Links:

• Download the PVLIB_Toobox for Matlab
• Download links for pvlib-python

Documentation Links:

• Documentation for PV_LIB Toolbox for Matlab

• Documentation for pvlib-python

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 (18876 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)

1. 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
2. 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
3. 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/
4. 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
5. 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
6. Bell, C (2022). Fluids Documentation., media.readthedocs.org, https://media.readthedocs.org/pdf/fluids/latest/fluids.pdf
7. 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
8. 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
9. 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
10. 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
11. 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
12. 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
13. 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
14. 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
15. 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
16. 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
17. 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
18. 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
19. 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
20. 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
21. 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
22. Beebe, NHF (2022). A Bibliography of Publications about the Python Scripting and Programming Language., ctan.math.utah.edu, https://scholar.google.comftp://ctan.math.utah.edu/pub/tex/bib/python.pdf
23. 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
24. 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

1. 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.
2. 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.
3. Holmgren, W, Lorenzo, T, Hansen, C, Mikofski, M, Krien, U, & … (2021). pvlib/pvlib-python: v0. 8.1., Jan
4. Ransome, S. (2021), VIRTUAL PVPearl Training School Brasov, Romania, http://www.steveransome.com/pubs/2021_07_PVCOST_Romania_Ransome_210706t11tobepresented.pdf
5. 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
6. 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
7. 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
8. 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
9. 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
10. Rinio, M (2021). PVcheck—A Software to Check Your Photovoltaic System. Energies, mdpi.com, https://www.mdpi.com/1996-1073/14/20/6757
11. 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/
12. 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
13. 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/
14. 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
15. 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
16. 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
17. 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/
18. 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
19. 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/
20. 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
21. 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
22. 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
23. 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
24. 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/
25. Bright, JM (2021). Introduction to synthetic solar irradiance., aip.scitation.org, https://doi.org/10.1063/9780735421820_001
26. 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
27. David, AY, Scott, J, Montgomery, W, & … (2021). Cloud Coverage Prediction to Improve Solar Power Management. …, scholarworks.calstate.edu, https://scholarworks.calstate.edu/downloads/xw42nf32n
28. 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/
29. 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/
30. 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
31. 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
32. 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
33. 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
34. 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
35. 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
36. 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
37. 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
38. 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/
39. 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
40. 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
41. 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/
42. 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
43. 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/
44. 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/
45. 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
46. 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
47. 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
48. 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
49. Westbrook, O (2021). Your P Values Are Wrong. 2021 IEEE 48th Photovoltaic Specialists …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9519076/
50. 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
51. 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/
52. 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
53. 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/
54. 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
55. 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/
56. 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
57. 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/
58. 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
59. 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/
60. 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/
61. 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/
62. 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
63. 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
64. 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
65. 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
66. 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
67. 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
68. 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
69. 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/
70. 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/
71. 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/
72. 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
73. 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
74. 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/
75. 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/
76. 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/
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16. Ospina-Metaute, CC, Betancur, E, & … (2020). Optimization of the orientation of vertical surfaces based on geographic parameters for solar energy harvesting. Workshop on …, Springer, https://doi.org/10.1007/978-3-030-61834-6_39
17. Hansen, C, Jordan, D, Deceglie, M, Gunda, T, & … (2020). Data Cleaning for Degradation Analyses.., osti.gov, https://www.osti.gov/servlets/purl/1821842
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19. Oh, S, Figgis, BW, & Rashkeev, S (2020). Effects of thermophoresis on dust accumulation on solar panels. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X20310069
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36. Melo, KB, Moreira, HS, & Villalva, MG (2020). Influence of Solar Position Calculation Methods Applied to Horizontal Single-Axis Solar Trackers on Energy Generation. Energies, mdpi.com, https://www.mdpi.com/779496
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38. Pouchain, F, Peronato, G, & Meunier, G (2020). A Flexible GIS-based Computational Framework for the Early-Design of District Energy Networks., proceedings.ises.org, http://proceedings.ises.org/paper/eurosun2020/eurosun2020-0100-Peronato.pdf
39. Willockx, B, Herteleer, B, & Cappelle, J (2020). Combining photovoltaic modules and food crops: first agrovoltaic prototype in Belgium. Renewable Energy & Power …, lirias.kuleuven.be, https://lirias.kuleuven.be/3183062?limo=0
40. Deceglie, MG, Silverman, TJ, & … (2020). Light and elevated temperature induced degradation (LeTID) in a utility-scale photovoltaic system. IEEE Journal of …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/9097396/
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43. Neves, LA, Leite, GC, & … (2020). ANÁLISE DO DESEMPENHO DE CÉLULAS FOTOVOLTAICAS ORGÂNICAS E INORGÂNICAS SOB DIFERENTES MASSAS DE AR UTILIZANDO SIMULAÇÃO …. … de Energia Solar …, anaiscbens.emnuvens.com.br, https://anaiscbens.emnuvens.com.br/cbens/article/view/730
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51. 山形裕貴, 野中尋史, & 山田昇 (2020). 深層強化学習による追尾式両面受光太陽電池アレイの最適制御. 北陸信越支部総会·講演会 講演論文集 …, jstage.jst.go.jp, https://www.jstage.jst.go.jp/article/jsmehs/2020.57/0/2020.57_D031/_article/-char/ja/
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166. Copping, AE, Green, RE, Cavagnaro, RJ, Jenne, DS, & … (2020). Powering the Blue Economy-Ocean Observing Use Cases Report., osti.gov, https://www.osti.gov/biblio/1700536
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168. Bloch, LB (2020). Optimal path to a high share of distributed PV in low-voltage distribution grids., infoscience.epfl.ch, https://infoscience.epfl.ch/record/279521
169. Dimanchev, E, Hodge, J, & Parsons, J (2020). Two-Way Trade in Green Electrons: Deep Decarbonization of the Northeastern US and the Role of Canadian Hydropower., dspace.mit.edu, https://dspace.mit.edu/handle/1721.1/130577
170. Júnior, EFM, & Rüther, R (2020). The influence of the solar radiation database and the photovoltaic simulator on the sizing and economics of photovoltaic-diesel generators. Energy Conversion and Management, Elsevier, https://www.sciencedirect.com/science/article/pii/S0196890420302752
171. Pinna, A, & Massidda, L (2020). A procedure for complete census estimation of rooftop photovoltaic potential in urban areas. Smart Cities, mdpi.com, https://www.mdpi.com/795944
172. Hoyo, M Del, Rondanelli, R, & Escobar, R (2020). Significant decrease of photovoltaic power production by aerosols. The case of Santiago de Chile. Renewable Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S096014811931496X
173. Rougerie-Durocher, S, Philion, V, & Szalatnay, D (2020). Measuring and modelling of apple flower stigma temperature as a step towards improved fire blight prediction. Agricultural and Forest …, Elsevier, https://www.sciencedirect.com/science/article/pii/S0168192320302732
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175. 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
176. 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
177. 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
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185. 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
186. 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
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13. 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/
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16. 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/
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20. 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
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26. 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
27. 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
28. Zuiker, A (2019). A comparative study of PV simulation and machine learning models on a macrolevel and microlevel., dspace.library.uu.nl, https://dspace.library.uu.nl/handle/1874/395328
29. 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
30. 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/
31. 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/
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34. 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
35. 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/
36. 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
37. 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
38. 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/
39. 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
40. 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
41. 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
42. 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
43. 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
44. 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/
45. 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/
46. 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
47. 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/
48. 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
49. 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/
50. 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
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52. 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/
53. 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
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69. 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/
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71. 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
72. 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
73. Bruinewoud, M (2018). Assessing the performance of the Utrecht Science Park PV system., dspace.library.uu.nl, https://dspace.library.uu.nl/handle/1874/371121
74. 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
75. Klise, KA, & Hart, D (2018). Pecos and Canary Webinar.., osti.gov, https://www.osti.gov/servlets/purl/1511107
76. 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
77. 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
78. 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
79. 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
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81. 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/
82. 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
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85. 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
86. 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
87. 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/
88. 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/
89. Stein, J (2018). FY2018 PV Lifetime Annual Report.., osti.gov, https://www.osti.gov/servlets/purl/1484335
90. 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
91. 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
92. 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
93. Prieto, E Castillo (2018). Predicción de Energía Fotovoltaica a partir de Ensembles NWP., repositorio.uam.es, https://repositorio.uam.es/handle/10486/685414
94. 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
95. 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
96. Edition, F (2018). A Solar Design Manual for Alaska., acep.uaf.edu, https://acep.uaf.edu/media/260463/EEM-01255_SolarDesignManual_5thEd201805.pdf
97. Nagel, J (2018). Optimization of Energy Supply Systems., Springer, https://doi.org/10.1007/978-3-319-96355-6
98. 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/
99. 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
100. 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
101. 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
102. 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
103. 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
104. 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
105. 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
106. 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
107. Freitas, SRT (2018). Photovoltaic potential in building façades., repositorio.ul.pt, https://repositorio.ul.pt/handle/10451/34868
108. 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
109. 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
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2017

1. 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/
2. Stein, J (2017). PV Performance Modeling Methods and Practices.., osti.gov, https://www.osti.gov/servlets/purl/1474242
3. Stein, J (2017). Challenges of PV Degradation Analysis: PVLIB and Performance Data Analysis.., osti.gov, https://www.osti.gov/servlets/purl/1482294
4. Ransome, S. and Sutterlueti, J. (2017). “OPTIMUM USE OF THE LOSS FACTORS MODEL (LFM) FOR IMPROVED PV PERFORMANCE MODELLING”, 13th PVSAT 2017 Bangor, UK,
   http://www.steveransome.com/PUBS/2017_PVSAT13_Ransome.pdf
5. 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
6. Gurupira, T, & Rix, AJ (2017). Pv simulation software comparisons: Pvsyst, nrel sam and pvlib,(january).
7. 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
8. Stein, J (2017). PV System Performance Modeling.., osti.gov, https://www.osti.gov/servlets/purl/1418248
9. 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
10. 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/
11. 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/
12. Hansen, CW, Stein, J, & Riley, DM (2017). 2017 IEEE PVSC tutorial PV system modeling.., osti.gov, https://www.osti.gov/servlets/purl/1513607
13. Gurupira, T, & Rix, A (2017). PV simulation software comparisons: PVSYST. NREL SAM AND PVLIB
14. 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/
15. 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
16. 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/
17. 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/
18. Klise, KA (2017). Pecos Open Source Software for PV Performance Monitoring.., osti.gov, https://www.osti.gov/servlets/purl/1457957
19. 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
20. 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/
21. 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/
22. 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/
23. 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/
24. 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
25. 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/
26. 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
27. 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
28. 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/
29. 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
30. 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
31. 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
32. 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
33. 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/
34. 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/
35. Computação, C da (2017). Mateus MS do Nascimento., ppgc.ufpel.edu.br, https://ppgc.ufpel.edu.br/documentos/andamento/MATEUS_MOREIRA_SILVEIRA_DO_NASCIMENTO_-_Otimizacao_dos_Padroes_de_Acesso_a_Mem_V2BIAOb.pdf
36. 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
37. Steiner, M, Gerstmaier, T, & Bett, AW (2017). Concentrating photovoltaic systems. The Performance of Photovoltaic (PV) …, Elsevier, https://www.sciencedirect.com/science/article/pii/B9781782423362000100
38. 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/
39. Hamann, HF (2017). A multi-scale, multi-model, machine-learning solar forecasting technology., osti.gov, https://www.osti.gov/biblio/1395344
40. 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
41. 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
42. 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
43. 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

1. 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/
2. 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/
3. 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
4. Stein, J (2016). 2016 PVLIB Users Group Meeting.., osti.gov, https://www.osti.gov/servlets/purl/1514526
5. Ransome, S, Stein, J, Holmgren, W, & … (2016). PV Performance modelling with PVPMC/PVLIB. PVSAT12, pdfs.semanticscholar.org, https://pdfs.semanticscholar.org/516e/d000fa1e9910668d6960c05c2db2816e3e27.pdf
6. 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/
7. 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
8. Deline, C, DiOrio, N, Jordan, D, & Toor, F (2016). Progress & frontiers in PV performance., osti.gov, https://www.osti.gov/biblio/1327483
9. 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/
10. Klise, GT (2016). PV System Reliability: An O&M Perspective.., osti.gov, https://www.osti.gov/servlets/purl/1346103
11. 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/
12. 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/
13. Klise, KA, & Stein, J (2016). Performance Monitoring using Pecos Version 0.1.., osti.gov, https://www.osti.gov/servlets/purl/1734479
14. 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/
15. 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
16. 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
17. 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/
18. Lassila, J, Tikka, V, Haapaniemi, J, Child, M, Breyer, C, & … (2016). Nationwide photovoltaic hosting capacity in the Finnish electricity distribution system. … Photovoltaic Solar Energy …
19. 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
20. 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
21. Tomažič, T (2016). Napovedovanje dnevne proizvodnje električne energije sončnih elektrarn., eprints.fri.uni-lj.si, http://eprints.fri.uni-lj.si/3650/
22. 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
23. 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/
24. 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
25. 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
26. 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
27. 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

1. Holmgren, WF, Andrews, RW, & … (2015). PVLIB python 2015. 2015 ieee 42nd …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/7356005/
2. Ellis, A (2015). Large-Scale Photovoltaics deployment.., osti.gov, https://www.osti.gov/servlets/purl/1333247
3. 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
4. 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/
5. Stein, JS (2015). FY15 Final Technical Report for DOE SunShot., osti.gov, https://www.osti.gov/servlets/purl/1232610
6. 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/
7. Stein, J. S. and M. Green (2015). Novel strategies for PV system monitoring. PV-Tech Power. London, UK, Solar Media. 02.
8. 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

1. 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.
2. 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
3. Stein, JS, & Toolbox, P (2014). Sandia National Laboratories: Albuquerque. NM, USA
4. 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
5. 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
6. 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

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