Machine Learning Approach on Time Series for PV-Solar Energy. (16th August 2022)

• Record Type: Journal Article
• Authors: Sivakumar, S.; Neeraja, B.; Jamuna Rani, M.; Anandaram, Harishchander; Ramya, S.; Padhan, Girish; Gurusamy, Saravanakumar
• Other Names: Chelladurai Samson Jerold Samuel Academic Editor.
• Is Part Of: Advances in materials science and engineering. Volume 2022(2022)
• Publication Date: 2022-08-16
• Subjects: Materials science -- Periodicals; Materials science; Periodicals; 620.11
• DOI: 10.1155/2022/6458377
• Languages: English
• ISSNs: 1687-8434

Abstract

Abstract : Solar energy is the kind of alternative energy that a photovoltaic (PV) system may generate. The PV system depends on the environmental conditions present at the moment, such as the humidity level, surface temperature of the PV system, and wind speed. These are the most important characteristics to consider when calculating the amount of electricity generated by the PV system. Therefore, the calculation of power production is not a simple task. In the meanwhile, we are working on developing the machine learning algorithm that will be used to estimate electricity production using the fundamental characteristics that are now accessible. Now, we begin the process of machine learning by developing a time series model since the essential parameters will change over time. We get preliminary information from four distinct locations around the country. We arrived at this conclusion by using the ANN and regression modeling approaches for best to create the most significant overall performance across all cities.