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).

Download Links:

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 (21665 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.
  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
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2020

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  158. Pun, KB (2020). Short term forecasting of solar power with machine learning and time series techniques., soar.wichita.edu, https://soar.wichita.edu/handle/10057/19764
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  161. Shaffery, P, Habte, A, Netto, M, Andreas, A, & Krishnan, V (2020). Automated construction of clear-sky dictionary from all-sky imager data. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X20311117
  162. Jiménez, JR Pungil (2020). Flujos de potencia probabilísticos considerando centrales fotovoltaicas empleando el método de montecarlo., bibdigital.epn.edu.ec, http://bibdigital.epn.edu.ec/handle/15000/21218
  163. Freitas, J de Sousa, Cronemberger, J, Soares, RM, & … (2020). Modeling and assessing BIPV envelopes using parametric Rhinoceros plugins Grasshopper and Ladybug. Renewable Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0960148120308417
  164. Zambrano, AF, & Giraldo, LF (2020). Solar irradiance forecasting models without on-site training measurements. Renewable Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0960148120301142
  165. Kong, W, Jia, Y, Dong, ZY, Meng, K, & Chai, S (2020). Hybrid approaches based on deep whole-sky-image learning to photovoltaic generation forecasting. Applied Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0306261920313465
  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
  167. Stainsby, W, Zimmerle, D, & Duggan, GP (2020). A method to estimate residential PV generation from net-metered load data and system install date. Applied Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0306261920304074
  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
  174. Yang, D, Alessandrini, S, Antonanzas, J, & … (2020). Verification of deterministic solar forecasts. Solar Energy, Elsevier, https://www.sciencedirect.com/science/article/pii/S0038092X20303947
  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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  182. Pravettoni, M (2020). Module Deployment and Energy Rating. Solar Cells and Modules, Springer, https://doi.org/10.1007/978-3-030-46487-5_10
  183. 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
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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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2019

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  6. 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
  7. 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/
  8. 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/
  9. 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
  10. 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
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  12. 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
  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/
  14. 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/
  15. 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
  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/
  17. 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
  18. 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
  19. 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/
  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
  21. Patel, SS, & Rix, AJ (2019). Water surface albedo modelling for floating PV plants. Europe, sasec.org.za, https://sasec.org.za/papers2019/8.pdf
  22. Carmignani, CK (2019). CO School of Mines-Sandia-PV & Materials Tech-Python.., osti.gov, https://www.osti.gov/servlets/purl/1644475
  23. Silva, MK (2019). Estudo de modelos matemáticos para análise da radiação solar e desenvolvimento de ferramenta para modelagem e simulação de sistemas fotovoltaicos., [sn]
  24. 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
  25. 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
  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.
  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/
  32. 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
  33. 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
  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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  148. Yang, J (2019). Designing Deployment Policies to Maximize the Co-benefits of China’s Clean Energy Transition., search.proquest.com, https://search.proquest.com/openview/02ef8875db78344e045b7e5c7087ccda/1?pq-origsite=gscholar&cbl=18750&diss=y
  149. 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
  150. Castillo, A, Flicker, J, Hansen, CW, & … (2019). Stochastic optimisation with risk aversion for virtual power plant operations: a rolling horizon control. IET Generation …, ieeexplore.ieee.org.
  151. 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
  152. 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
  153. 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
  154. 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
  155. Li, D (2019). Micro optics for micro hybrid concentrator photovoltaics., dspace.mit.edu, https://dspace.mit.edu/handle/1721.1/123563
  156. 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
  157. 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
  158. Nyirenda, E (2019). Conjunctive Operation of Hydro and Solar PV Power with Pumped Storage at Kafue Gorge Power Station (Zambia)., diva-portal.org, https://www.diva-portal.org/smash/record.jsf?pid=diva2:1334218
  159. 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
  160. Besseau, R (2019). Analyse de cycle de vie de scénarios énergétiques intégrant la contrainte d’adéquation temporelle production-consommation., theses.fr, https://www.theses.fr/2019PSLEM068

2018

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  2. Hansen, C (2018). What? s new in PVLib and pvlib-python?.., osti.gov, https://www.osti.gov/servlets/purl/1513354
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  5. Kallio, V, & Riihelä, A (2018). Comparative Validation of Clear Sky Irradiance Models over Finland. IGARSS 2018-2018 IEEE International …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8519270/
  6. 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/
  7. Riley, D, Stein, J, & Hansen, C (2018). 2018 IEEE PVSC Tutorial PV System Modeling.., osti.gov, https://www.osti.gov/servlets/purl/1511110
  8. 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/
  9. 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
  10. Stein, JS, & Lavrova, O (2018). Final Project Report: PV Stakeholder Engagement Initiatives., osti.gov, https://www.osti.gov/biblio/1481543
  11. Klise, KA (2018). Pecos-Open Source Software for PV System Monitoring.., osti.gov, https://www.osti.gov/servlets/purl/1525601
  12. Hansen, C (2018). Photovoltaic And Renewable Energy Systems Research.., osti.gov, https://www.osti.gov/servlets/purl/1806947
  13. 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/
  14. 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/
  15. 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
  16. Ho, CK (2018). Solar Technology Research at Sandia: PV and CSP (presentation).., osti.gov, https://www.osti.gov/servlets/purl/1592672
  17. 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/
  18. 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/
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. Moreno, S Aguacil, Peronato, G, Lufkin, S, & … (2018). Assessment of the building-integrated photovoltaic potential in urban renewal processes in the Swiss context: complementarity of urban-and architectural-scale …. Smart and Healthy …, upcommons.upc.edu, https://upcommons.upc.edu/handle/2117/130986
  27. Lee, KH, Araki, K, Kojima, N, & … (2018). Achieving wide-acceptance angle and high on-axis performance static low-concentration module using hybrid lens arrays. AIP Conference …, aip.scitation.org, https://doi.org/10.1063/1.5053514
  28. Stein, J (2018). Opportunities for New and Innovative Photovoltaic Modules and Systems.., osti.gov, https://www.osti.gov/servlets/purl/1514776
  29. Ascencio-Vásquez, J, Brecl, K, & … (2018). Köppen-Geiger-photovoltaic climate classification. 2018 IEEE 7th World …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8547952/
  30. Chen, D, Breda, J, & Irwin, D (2018). Staring at the sun: a physical black-box solar performance model. Proceedings of the 5th Conference on …, dl.acm.org, https://doi.org/10.1145/3276774.3276782
  31. Smithpeter, I (2018). ASES National Solar Conference 2018 A Low-Cost IoT Approach to Real-Time Cloud Motion Detection., proceedings.ises.org, http://proceedings.ises.org/paper/solar2018/solar2018-0012-Smithpeter.pdf
  32. Dallapiccola, M, Ingenhoven, P, Moser, D, & … (2018). Simplified Method for Partial Shading Losses Calculation for Series Connected PV Modules with Experimental Validation. 35th Eur. Photovolt. Sol …, researchgate.net, https://www.researchgate.net/profile/Mattia-Dallapiccola/publication/330307560_SIMPLIFIED_METHOD_FOR_PARTIAL_SHADING_LOSSES_CALCULATION_FOR_SERIES_CONNECTED_PV_MODULES_WITH_EXPERIMENTAL_VALIDATION/links/5c38486e299bf12be3be484c/SIMPLIFIED-METHOD-FOR-PARTIAL-SHADING-LOSSES-CALCULATION-FOR-SERIES-CONNECTED-PV-MODULES-WITH-EXPERIMENTAL-VALIDATION.pdf
  33. Irigoyen, A Berrueta, Heck, M, & … (2018). Combined dynamic programming and region-elimination technique algorithm for optimal sizing and management of lithium-ion batteries for photovoltaic plants. … Energy, 228 (2018 …, academica-e.unavarra.es, https://academica-e.unavarra.es/handle/2454/33235
  34. 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
  35. Mountain, B, & Kars, A (2018). Using electricity bills to shine a light on rooftop solar photovoltaics in Australia: A comparison of prices, volumes and socio-economic rank of households with …., vuir.vu.edu.au, https://vuir.vu.edu.au/41864/1/Solar_Citizens_FINAL_Report_23_NOVEMBER_2018.pdf
  36. Hilpert, S, Kaldemeyer, C, Krien, U, Günther, S, & … (2018). The Open Energy Modelling Framework (oemof)-A new approach to facilitate open science in energy system modelling. Energy strategy …, Elsevier, https://www.sciencedirect.com/science/article/pii/S2211467X18300609
  37. Lee, KH, Araki, K, & Yamaguchi, M (2018). Achieving high efficiency static low-concentration photovoltaic module using hybrid lens arrays. … PVSC, 28th PVSEC & 34th EU …, ieeexplore.ieee.org, https://ieeexplore.ieee.org/abstract/document/8547928/
  38. Sehnem, JM, Michels, L, & … (2018). SIMULAÇÕES NUMÉRICAS PARA DETERMINAÇÃO DE INCLINAÇÕES ÓTIMAS PARA MÓDULOS FOTOVOLTAICOS. … de Energia Solar …, anaiscbens.emnuvens.com.br, http://anaiscbens.emnuvens.com.br/cbens/article/view/723
  39. Groenendyk, D (2018). cons2_Python Documentation., media.readthedocs.org, https://media.readthedocs.org/pdf/cons2-python/master/cons2-python.pdf
  40. 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
  41. 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/
  42. 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
  43. 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
  44. 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/
  45. 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
  46. 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
  47. 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/
  48. 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
  49. 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
  50. 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/
  51. 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
  52. 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
  53. 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/
  54. Stein, J (2018). Solar PV Performance and New Technologies in Northern Latitude Regions.., osti.gov, https://www.osti.gov/servlets/purl/1510188
  55. 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
  56. 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
  57. 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
  58. 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
  59. Nascimento, MMS (2018). Optimization of memory usage in quantum computing simulation., repositorio.ufpel.edu.br, http://repositorio.ufpel.edu.br:8080/handle/prefix/4355
  60. Stein, J (2018). The Disruption of Future PV Developments.., osti.gov, https://www.osti.gov/servlets/purl/1512004
  61. 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/
  62. Gurupira, TL (2018). Evaluation and optimisation of photovoltaic (PV) plant designs., scholar.sun.ac.za, http://scholar.sun.ac.za/handle/10019.1/103499
  63. 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
  64. 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
  65. 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
  66. Gamarro, H, Gonzalez, JE, & … (2018). Urban WRF-Solar Validation and Potential for Power Forecast in New York City. Energy …, asmedigitalcollection.asme.org, https://asmedigitalcollection.asme.org/ES/proceedings-abstract/ES2018/V001T09A001/269942
  67. 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
  68. 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
  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/
  70. 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/
  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.
  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
  80. 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/
  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
  83. DiOrio, NA, Blair, NJ, Freeman, JM, Habte, AM, & … (2018). Solar System Modeling at NREL., osti.gov, https://www.osti.gov/biblio/1477226
  84. 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
  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
  110. Gurupira, T, & Rix, A (2017). Pv simulation software comparisons: Pvsyst, nrel sam and pvlib. Conf.: SAUPEC, orcun.baslak.com.

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,
  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.
  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.