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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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