Global Solar Irradiance Time Series Prediction Using Long Short-Term Memory Network

Abstract

Global solar radiation estimation is increasingly acquiring more importance to ensure effective management and operation of solar energy systems as well as providing reliable information about the future power generation. In this study, the Long Short-Term Memory (LSTM) has been suggested to effectively model the daily solar radiation prediction problem. The effectiveness of the suggested method compared with the state of the art machine learning algorithms such as Decision Tree Regression, Random Forest Regression, Gradient Boosting and K-Nearest Neighbor. Daily solar irradiance sequential time series data in Çorum - Turkey between January1983 and December-2018 have been used to validate the effectiveness of the suggested LSTM method. The simulation outcomes demonstrate that LSTM method has generally better performance than the other machine learning models.

Keywords

• deep neural network
• time series forecasting
• long short-term memory
• solar irradiance
• machine learning