TY - GEN
T1 - NeuralPV: A Neural Network Algorithm for PV Power Forecasting
AU - Pervez, Imran
AU - Shi, Jian
AU - Ghazzai, Hakim
AU - Massoud, Yehia Mahmoud
N1 - KAUST Repository Item: Exported on 2023-07-24
PY - 2023/7/21
Y1 - 2023/7/21
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/693187
UR - https://ieeexplore.ieee.org/document/10181648/
U2 - 10.1109/iscas46773.2023.10181648
DO - 10.1109/iscas46773.2023.10181648
M3 - Conference contribution
BT - 2023 IEEE International Symposium on Circuits and Systems (ISCAS)
PB - IEEE
ER -
