Long-Term Solar Irradiance Forecasting Using Multilinear Predictors

Abstract

As the demand for crude oil is increasing every day, prices and pollution are both increasing in return, which has harmful effects on the environment. Thus, more attempts are being made to develop clean energy to rescue the planet and provide humanity with a cleaner energy source. As the renewable energy sector grows, new issues and challenges have emerged. Instability in electricity production from wind turbines, solar power plants, and dams creates challenges for energy transmission and storage systems. In order to achieve a more reliable and effective energy system, machine learning techniques have been used to forecast energy changes. Predicting the sun's Global Horizontal Irradiance (GHI) is one of the machine learning applications used in that sector. In this paper, many machine learning methods have been utilized, such as linear regression and long-short term memory (LSTM) methods to have long term GHI forecasting. Moreover, the significance of this paper is located in the way of prediction of the GHI irradiance prediction by using different levels of the linear regressors to find the best regressor level that provides the minimum error for the testing set based on cross-validation. Results showed that the regressor method provides a lower error compared with a single vanilla LSTM system for a shorter time computationally.