Forecasting approach for solar power based on weather parameters (Case study: East Kalimantan)

Abstract

Solar Energy is the most popular among several clean energies. As a tropical country, Indonesia has big opportunity to develop solar power, particularly in East Kalimantan which spans around the equator. Solar energy generation, however, is influenced by weather parameters which give uncertain values of the amount of the captured energy. Therefore, this research is conducted to overcome the effect of weather towards solar energy. The aim of this research is to examine the model for sun power forecasting based on the data. The Artificial Neural Network (ANN) and Multiple Linear Regression have taken as the approach models to determine energy forecasting. This study used five input variables; temperature, precipitation level, humidity, wind speed, and surface pressure, while the solar radiation was taken as the output variable. Moreover, the daily solar power and weather data from East Kalimantan has been taken along the period of 27thJuly 2018 - 28thJuly 2021. The result of this study showed that the RMSE of ANN was slightly similar with the multiple linear regression methods which were calculated by 160.26 and 160.46 respectively. However, the ANN is preferable to use in the solar energy forecasting since the tendency of nonlinearity of the climate data. Keywords: ANN, Regression, Solar Energy.