A Comparative Analysis of Artificial Neural Networks for Photovoltaic Power Forecast Using Remotes and Local Measurements

Sofia M.A. Lopes, Elmer P.T. Cari, Shima Hajimirza

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

The inclusion of photovoltaic systems in distribution networks has raised the importance of the prediction of photovoltaic power for safe planning and operation. Artificial neural networks (ANNs) have been used in this task due to its capacity of representing nonlinearities. However, the profile of the data used may affect the forecast accuracy. This manuscript reports on a comparative analysis of the performance of four neural network models for photovoltaic power forecast regarding their input dataset. Four sets composed of photovoltaic power data (local measurements) and external weather data (remote measurements) were used, and the networks were validated through actual measurements from a photovoltaic micro plant. The ANN that dealt with only weather data showed a good level of accuracy, being a useful tool for the feasibility analysis of new photovoltaic projects. In addition, the approach that used only photovoltaic power data has excelled and can be used in electric sector companies.

Original languageEnglish
Article number021007
JournalJournal of Solar Energy Engineering, Transactions of the ASME
Volume144
Issue number2
DOIs
StatePublished - Apr 2022

Keywords

  • artificial neural networks
  • measurements
  • photovoltaic power forecasting
  • solar energy

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