Improving PV power plant forecast accuracy: A hybrid deep learning approach compared across short, medium, and long-term horizons

Abstract

This research paper investigates the use of Long Short-Term Memory (LSTM) and Grid Search Algorithm (GSA)-LSTM methods to forecast PV power output in different horizons. The study proposes precise hyperparameters for the LSTM network for improved performance prediction. Initially, LSTM was evaluated solely, and three different scenarios were investigated to obtain the best LSTM network hyperparameter settings for various horizons using a sensitivity study. Afterwards, LSTM was coupled with GSA to optimize LSTM network hyperparameters, which enhanced prediction accuracy and minimized error. The proposed methodology identified the most effective values for each hyperparameter based on different forecasting horizons. Additionally, Spearman Correlation Coefficient (SCC) and Pearson Correlation Coefficient (PCC) were deployed to determine the relationship between input data and target as well as identifying optimum lag value which has substantial effect on LSTM network. Optimal lag values of 150 and 280 were determined following the strong correlation between the data and target. LSTM and GSA-LSTM methods were compared with each other at various horizons and it was demonstrated that GSA-LSTM is superior by improving MSE at peak points by 10%, 30%, and 34% for 12 hours, 3 days, and 2 weeks horizons respectively. Comparing the proposed methodology with other studies in the literature revealed that the current study is capable of predicting PV power output for the 1 hour ahead horizon with significantly higher accuracy and with maximum improvement of about 28%.