A Comparative Study of the Effect of Weather Conditions on Solar Irradiance Forecasting Using Various Methods in Yogyakarta, Indonesia

Abstract

Listen

Translate article

Abstract The government’s plan to build utility-scale solar power plants for the next decade will impact the high penetration of solar energy into the grid. This problem is associated with the intermittent nature of solar electricity, which can disrupt the grid system’s stability and reliability if it is not tackled. Furthermore, the complicated weather conditions in Indonesia make this variable challenging to predict. Therefore, one of the solutions offered is the solar irradiance forecasting technique that helps the operator during planning. This paper will look at the differences in these weather characteristics in predicting solar irradiance using three forecasting methods: LSTM, RNN, and ARIMA. The data is taken from direct measurements installed in the center of the island of Java, namely Yogyakarta. The result of this work shows that among LSTM, RNN, and ARIMA forecasting models, the highest forecast accuracy has been performed by LSTM with RMSE, MAPE, MAE, and R2 are 33.01 W/m2, 7.72%, 24.51 W/m2 and 0.96 respectively. Subsequently, partially cloudy, cloudy, or rainy weather has a worse predicting performance than weather with a sunny or clear sky. However, they are still regarded as reasonable forecasts.