Statistical measures are numerical quantities or metrics that reveal details about the distribution, central tendency, variability, linkages, and other properties of a dataset. These metrics aid statisticians, researchers, and analysts in summarizing, describing, and interpreting data, making it simpler to develop judgements and take action. Scientific, commercial, social science, and research disciplines all employ statistical measurements, which are crucial instruments in data analysis.
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.