Comparative Study of Photovoltaic Power Forecasting Methods
Resumo
Electricity consumption is growing rapidly worldwide. Renewable energy resources, such as solar energy, play a crucial role in this scenario, contributing to satisfy demand sustainability. Although the share of Photovoltaic (PV) power generation has increased in the past years, PV systems are quite sensitive to climatic and meteorological conditions, leading to undesirable power production variability. In order to improve energy grid stability, reliability, and management, accurate forecasting models that relate operational conditions to power output are needed. In this work we evaluate the performance of regression methods applied to forecast short term (next day) energy production of a PV Plant. Specifically, we consider five regression methods and different configurations of feature sets. Our results suggest that MLP and SVR provide the best forecasting results, in general. Also, although features based on different solar irradiance levels play a key role in predicting power generation, the use of additional features can improve prediction results.
Palavras-chave:
PV Power Forecasting, Photovoltaic Systems, Regression
Referências
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Kaplani, E. and Kaplanis, S. (2014). Thermal modelling and experimental assessment of the dependence of PV module temperature on wind velocity and direction, module orientation and inclination. Solar Energy, 107:443–460.
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Nonnenmacher, L., Kaur, A., and Coimbra, C. F. (2014). Verification of the SUNY direct normal irradiance model with ground measurements. Solar Energy, 99:246–258.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Pedro, H. T. C. and Coimbra, C. F. M. (2012). Assessment of forecasting techniques for solar power production with no exogenous inputs. Solar Energy, 86(7):2017–2028.
Raza, M. Q., Nadarajah, M., and Ekanayake, C. (2016). On recent advances in PV output power forecast. Solar Energy, 136:125–144.
Souza-Echer, M. P., Pereira, E. B., Bins, L. S., and Andrade, M. A. (2006). A simple method for the assessment of the cloud cover state in high-latitude regions by a groundbased digital camera. Journal of Atmospheric and Oceanic Technology, 23(3):437– 447.
Statista (2019). Statista - global business platform, world energy outlook 2019. https: //www.statista.com/outlook/256/115/household-appliances/ brazil. Accessed: 2020-03-17.
Vapnik, V. N. (1998). Statistical Learning Theory. Wiley-Interscience.
Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. A. (1984). Classification and regression trees. CRC press.
Chollet, F. et al. (2015). Keras. https://keras.io.
Chow, S. K., Lee, E. W., and Li, D. H. (2012). Short-term prediction of photovoltaic energy generation by intelligent approach. Energy and Buildings, 55:660–667.
Das, U. K., Tey, K. S., Seyedmahmoudian, M., Mekhilef, S., Idris, M. Y. I., Van Deventer, W., Horan, B., and Stojcevski, A. (2018). Forecasting of photovoltaic power generation and model optimization: A review. Renewable and Sustainable Energy Reviews, 81(April 2017):912–928.
De Giorgi, M. G., Congedo, P. M., and Malvoni, M. (2014). Photovoltaic power forecasting using statistical methods: Impact of weather data. IET Science, Measurement and Technology, 8(3):90–97.
Ding, M., Wang, L., and Bi, R. (2011). An ANN-based approach for forecasting the power output of photovoltaic system. Procedia Environmental Sciences, 11(PART C):1308–1315.
Fernandez-Jimenez, L. A., Muñoz-Jimenez, A., Falces, A., Mendoza-Villena, M., GarciGarrido, E., Lara-Santillan, P. M., Zorzano-Alba, E., and Zorzano-Santamaria, P. J. (2012). Short-term power forecasting system for photovoltaic plants. Renewable Energy, 44:311–317.
Gielen, D., Boshell, F., Saygin, D., Bazilian, M. D., Wagner, N., and Gorini, R. (2019). The role of renewable energy in the global energy transformation. Energy Strategy Reviews, 24(January):38–50.
Haykin, S. (1998). Neural Networks: A Comprehensive Foundation. Prentice Hall PTR, USA, 2nd edition.
Huang, Y., Lu, J., Liu, C., Xu, X., Wang, W., and Zhou, X. (2010). Comparative study of power forecasting methods for PV stations. 2010 International Conference on Power System Technology: Technological Innovations Making Power Grid Smarter, POWERCON2010, pages 1–6.
IEA (2017). International energy agency, data and statistics 2017. https: //www.iea.org/data-and-statistics/data-tables/?country= WORLD&energy=Electricity&year=2017. Accessed: 2020-03-17.
IEA (2019). International energy agency, world energy outlook 2019. https://www. iea.org/reports/world-energy-outlook-2019/electricity. Accessed: 2020-03-17.
Inman, R. H., Pedro, H. T., and Coimbra, C. F. (2013). Solar forecasting methods for renewable energy integration. Progress in Energy and Combustion Science, 39(6):535– 576.
Kaplani, E. and Kaplanis, S. (2014). Thermal modelling and experimental assessment of the dependence of PV module temperature on wind velocity and direction, module orientation and inclination. Solar Energy, 107:443–460.
Kingma, D. P. and Ba, J. (2014). Adam: A method for stochastic optimization.
Kirkpatrick, C. and Dahlquist, J. (2006). Technical Analysis: The Complete Resource for Financial Market Technicians. FT Press, first edition.
Kudo, M., Takeuchi, A., Nozaki, Y., Endo, H., and Jiro, S. (2009). Forecasting electric power generation in a photovoltaic power system for an energy network. Electrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi), 167(4):16– 23.
Leva, S., Dolara, A., Grimaccia, F., Mussetta, M., and Ogliari, E. (2017). Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power. Mathematics and Computers in Simulation, 131:88–100.
Lo Brano, V., Ciulla, G., and Di Falco, M. (2014). Artificial neural networks to predict the power output of a PV panel. International Journal of Photoenergy, 2014.
Lorenz, E., Hurka, J., Heinemann, D., and Beyer, H. G. (2009). Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2(1):2–10.
Mahtta, R., Joshi, P. K., and Jindal, A. K. (2014). Solar power potential mapping in India using remote sensing inputs and environmental parameters. Renewable Energy, 71:255–262.
Malvoni, M., De Giorgi, M. G., and Congedo, P. M. (2017). Forecasting of PV Power Generation using weather input data-preprocessing techniques. Energy Procedia, 126:651–658.
Mellit, A., Massi Pavan, A., and Lughi, V. (2014). Short-term forecasting of power production in a large-scale photovoltaic plant. Solar Energy, 105:401–413.
Muhammad Ehsan, R., Simon, S. P., and Venkateswaran, P. R. (2017). Day-ahead forecasting of solar photovoltaic output power using multilayer perceptron. Neural Computing and Applications, 28(12):3981–3992.
Nonnenmacher, L., Kaur, A., and Coimbra, C. F. (2014). Verification of the SUNY direct normal irradiance model with ground measurements. Solar Energy, 99:246–258.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Pedro, H. T. C. and Coimbra, C. F. M. (2012). Assessment of forecasting techniques for solar power production with no exogenous inputs. Solar Energy, 86(7):2017–2028.
Raza, M. Q., Nadarajah, M., and Ekanayake, C. (2016). On recent advances in PV output power forecast. Solar Energy, 136:125–144.
Souza-Echer, M. P., Pereira, E. B., Bins, L. S., and Andrade, M. A. (2006). A simple method for the assessment of the cloud cover state in high-latitude regions by a groundbased digital camera. Journal of Atmospheric and Oceanic Technology, 23(3):437– 447.
Statista (2019). Statista - global business platform, world energy outlook 2019. https: //www.statista.com/outlook/256/115/household-appliances/ brazil. Accessed: 2020-03-17.
Vapnik, V. N. (1998). Statistical Learning Theory. Wiley-Interscience.
Publicado
20/10/2020
Como Citar
PELISSON, Angelo; COVOES, Thiago; SPENGLER, Anderson; JASKOWIAK, Pablo.
Comparative Study of Photovoltaic Power Forecasting Methods. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 17. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 555-566.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2020.12159.
