NeuralPV: A Neural Network Algorithm for PV Power Forecasting

Imran Pervez, Jian Shi, Hakim Ghazzai, Yehia Mahmoud Massoud

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Photovoltaic (PV) forecasting plays a major role in residential and industrial PV installation as well as penetration with the grid. An inaccurate PV power forecasting may result in increased monetary and energy losses. This study proposes a metaheuristic-based strategy for accurate PV power forecasting using a heuristic-based data-driven PV model. The proposed algorithm integrates a dense explorative strategy with the existing PV equation knowledge by a multilayer perceptron (MLP) network with Sigmoid activation functions to predict the best coefficients for the inputs of the data-driven PV model. The proposed method is compared to a recently proposed metaheuristic algorithm, the artificial hummingbird optimizer algorithm (AHOA). The comparison is performed for inside distribution (ID) and out-of-distribution (OOD) irradiance datasets and with varying temperatures. The results prove that the proposed NN-based algorithm achieves higher accuracy in PV power parameter prediction and hence forecasting.
Original languageEnglish (US)
Title of host publication2023 IEEE International Symposium on Circuits and Systems (ISCAS)
PublisherIEEE
DOIs
StatePublished - Jul 21 2023

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