PV solar power forecasting based on hybrid MFFNN-ALO

Abstract

Clean energy sources such as photovoltaic (PV) panels are widely employed. However, their performance is affected by the surroundings. A hybrid optimization technique that comprised an ant lion optimizer (ALO) and artificial neural network (ANN) is presented in this study, to forecast the PV cell temperature and output power. The optimizer’s major purpose was to create and improve an ANN approach that was based on training and forecasting. The ALO was used as MVO and GA to obtain the optimal hidden layers neurons number, weights, and biases, of the proposed ANNs. The accuracy of the multilayer feed forward neural networks (MFFNN) was evaluated using the data from the MFFNN-MVO, MFFNN-GA and MFFNN-ALO models. The panel output power and temperature were regulated by three variables: solar irradiation, ambient temperature, and wind speed. The Saudi Arabia, Shaqra City PV station with 4kW output power is the source of the two years testing and training. For the MFFNN-GA, MFFNN-MVO, and MFFNN-ALO models, the NRMSE for DC power predicting compared to 2019 observed data was 2.781E-3, 7.11E-4, and 6.08 E-04, respectively.