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Design of experiments using artificial neural network ensemble for photovoltaic generation forecasting

Author

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  • Moreira, M.O.
  • Balestrassi, P.P.
  • Paiva, A.P.
  • Ribeiro, P.F.
  • Bonatto, B.D.

Abstract

In recent years, renewable and sustainable energy sources have attracted the attention of various investors and stakeholders, such as energy sector agents and even consumers. It is perplexing to observe and anticipate the required levels of photovoltaic generation, which are inherent tasks for such rapid insertion into the electric grid. This distributed/renewable generation must be integrated in a coordinated way such that there is no negative impact on the electric performance of the grid, increasing in the complexity of energy management. In this article, a methodology for photovoltaic generation forecasting is addressed for a horizon of one week ahead, using a new approach based on an artificial neural network (ANN) ensemble. Two main questions will be explored with this approach: how to select the ANNs, and how to combine them in the ensemble. The design of experiments (DOE) approach is applied to the photovoltaic time series factors and ANN factors. Then, a cluster analysis is performed to select the best networks. From this point on, a mixture (MDE) is employed to determine the ideal weights for the ensemble formation. The methodology is detailed throughout the paper and, based on the combination of forecasts, the photovoltaic generation was estimated for a specific panel set located in the state of Minas Gerais, Brazil, reaching the value of 4.7% for the weekly mean absolute percentage error. The versatility of the proposed method allowed the change of the number of factors to be used in the experimental arrangement, the forecast model, and the desired forecast horizon, and consequently enhancing the forecasting determination.

Suggested Citation

  • Moreira, M.O. & Balestrassi, P.P. & Paiva, A.P. & Ribeiro, P.F. & Bonatto, B.D., 2021. "Design of experiments using artificial neural network ensemble for photovoltaic generation forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    • Handle: RePEc:eee:rensus:v:135:y:2021:i:c:s1364032120307371
    DOI: 10.1016/j.rser.2020.110450
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    References listed on IDEAS

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    1. WenBo Xiao & Gina Nazario & HuaMing Wu & HuaMing Zhang & Feng Cheng, 2017. "A neural network based computational model to predict the output power of different types of photovoltaic cells," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-8, September.
    2. Deo, Ravinesh C. & Şahin, Mehmet, 2017. "Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 828-848.
    3. Lima, Francisco J.L. & Martins, Fernando R. & Pereira, Enio B. & Lorenz, Elke & Heinemann, Detlev, 2016. "Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks," Renewable Energy, Elsevier, vol. 87(P1), pages 807-818.
    4. Amrouche, Badia & Le Pivert, Xavier, 2014. "Artificial neural network based daily local forecasting for global solar radiation," Applied Energy, Elsevier, vol. 130(C), pages 333-341.
    5. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    6. Wen, Lulu & Zhou, Kaile & Yang, Shanlin & Lu, Xinhui, 2019. "Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting," Energy, Elsevier, vol. 171(C), pages 1053-1065.
    7. Theocharides, Spyros & Makrides, George & Livera, Andreas & Theristis, Marios & Kaimakis, Paris & Georghiou, George E., 2020. "Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing," Applied Energy, Elsevier, vol. 268(C).
    8. Cervone, Guido & Clemente-Harding, Laura & Alessandrini, Stefano & Delle Monache, Luca, 2017. "Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble," Renewable Energy, Elsevier, vol. 108(C), pages 274-286.
    9. Barbieri, Florian & Rajakaruna, Sumedha & Ghosh, Arindam, 2017. "Very short-term photovoltaic power forecasting with cloud modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 242-263.
    10. Qing, Xiangyun & Niu, Yugang, 2018. "Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM," Energy, Elsevier, vol. 148(C), pages 461-468.
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