The quantity of solar radiation that reaches the surface of the Earth from all directions, including direct sunlight and scattered light from the sky, is referred to as diffuse horizontal irradiance (DHI).
Published in Chapter:
Effective Planning of Renewable Energy System: Solar Radiation Prediction Case Study in Telangana State, India Using Machine Learning Approach
Copyright: © 2024
|Pages: 35
DOI: 10.4018/979-8-3693-0492-1.ch008
Abstract
The energy demand crisis is being faced by all the nations due to the rapid growth of the global economy. The conventional resources available on the Earth are finite. Burning these fossil fuels abundantly results in large-scale greenhouse gas emissions and significant environmental contamination. The generation of electricity using renewable energy sources has increased significantly in recent years. However, the power generation using renewable energy sources like solar, wind, etc., is weather dependent and highly erratic. In order to maintain system stability and to use renewable energy resources effectively, renewable power forecast is essential. For the effective planning of power network, three different machine learning algorithms (i.e., linear regression (LR), decision tree regression (DTR) and random-forest regression (RFR)) are used for predicting the solar radiation in Mahabubnagar, Telangana. All the three regression algorithms are evaluated in terms of statistical measures; random-forest regression algorithm provides best results.