Novel applications of various neural network models for prediction of photovoltaic system power under outdoor condition of mountainous region

Abstract

The geography and landscape of mountainous locations are frequently varied, which causes uneven solar radiation exposure in different places which leads to photovoltaic (PV) power generation variation drastically. Therefore accurate PV power prediction is essential for industries in optimize energy production, Energy Planning and Grid Integration, Energy Storage Integration in such diverse conditions. This study aims to predict the power produced by a 2.680 kWp PV under outdoor condition in mountainous region at different time intervals of 10 seconds, 1 minute, 30 minutes, 1 hour and 1 day using different data analytics models. The results show that the radial basis neural network was the most effective model for the intervals of 10 seconds, 1 minute, 30 minutes, and 1 day. Its RMSE was 19.11 W for the 10 -second interval, 22.83 W for the 1 -minute interval, 25.94 W for the 30 -minute interval, and 9.08 W for the 1 -day interval. With an RMSE of 23.22 W, the Kernel Ridge Regressor had the best performance for the 1 -hour period. This suggests that the model with only two parameters such as temperature and irradiance predict PV power more accurately. The findings can be applied to improve PV system design and performance by precisely forecasting the systems' power output in different weather scenarios.