TY - JOUR
T1 - A Comparative Analysis of Artificial Neural Networks for Photovoltaic Power Forecast Using Remotes and Local Measurements
AU - Lopes, Sofia M.A.
AU - Cari, Elmer P.T.
AU - Hajimirza, Shima
N1 - Publisher Copyright:
Copyright © 2021 by ASME.
PY - 2022/4
Y1 - 2022/4
N2 - 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.
AB - 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.
KW - artificial neural networks
KW - measurements
KW - photovoltaic power forecasting
KW - solar energy
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U2 - 10.1115/1.4053031
DO - 10.1115/1.4053031
M3 - Article
AN - SCOPUS:85127233179
SN - 0199-6231
VL - 144
JO - Journal of Solar Energy Engineering, Transactions of the ASME
JF - Journal of Solar Energy Engineering, Transactions of the ASME
IS - 2
M1 - 021007
ER -