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

Author:

Lopes Sofia M. A.1,Cari Elmer P. T.1,Hajimirza Shima2

Affiliation:

1. School of Engineering, University of São Paulo, São Carlos, São Paulo 13566-590, Brazil

2. Stevens Institute of Technology, Castle Point on Hudson, Hoboken, NJ 07030-5991

Abstract

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.

Funder

Fundação de Amparo à Pesquisa do Estado de São Paulo

Universidade de São Paulo

Publisher

ASME International

Subject

Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

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