Chapter one - Machine learning algorithms used for short-term PV solar irradiation and temperature forecasting at microgrid

Abstract

Microgrids (MGs) are considered an important part of the evolving power grid in terms of reliability, cost, and environmental impact of distributed generation systems. The main production resource in MGs is renewable energy (RE) sources which have grown even more recently. The energy management system in MGs has many features such as decreasing the power losses in transmission lines and flexibility of monitoring the distributed energy resources (DERs). However, the power output from the DERs may get badly affected due to the intermittent nature of RE connected with the MGs system. Therefore accurate short-term solar generation forecasting is an important issue in MG to predict the required amount of power to be dispatched by DERs, which can lead to economic advantages for end-users. In this chapter, a comparative study of different machine learning (ML) approaches has been applied for forecasting solar radiation and temperature. In the proposed method, the most applied models in ML such as linear regression, random forest (RF) regression, K-nearest neighbors, support vector machines (SVMs) models are compared. We assessed the performance of ML proposed by evaluation of the root mean square error, and then investigated the influences of the parameter’s techniques. The simulation result shows that RF and SVM perform well in the short-term forecasting of solar irradiation and temperature.