Machine learning models for renewable energy forecasting. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Tharani, Kusum; Kumar, Neeraj; Srivastava, Vishal; Mishra, Sakshi; Pratyush Jayachandran, M.
• Is Part Of: Journal of statistics & management systems. Volume 23:Issue 1(2020)
• Page Start: 171
• Page End: 180
• Publication Date: 2020-01-02
• Subjects: 62J05; Linear Regression -- Neural Network -- Random Forest -- Extra Trees -- Solar Insolation -- Photovoltaic -- Machine Learning; Statistics -- Periodicals; Mathematical models -- Periodicals; Mathematical models; Statistics; Periodicals; 519.5
• DOI: 10.1080/09720510.2020.1721636
• Languages: English
• ISSNs: 0972-0510

Abstract

Abstract: As the world unanimously works towards utilizing non-conventional energy for powering industries, households, vehicles, etc., one of the key limiting factors is the fluctuation of energy availability by non-conventional energy sources. The power generated by wind turbines or photovoltaic cells is dependent upon factors that can't be controlled such as wind speed, humidity, solar irradiance. In such a situation the integration of renewable energy into grids becomes difficult. Eventually, maintaining an equilibrium between the energy supply and demand can be erratic. This calls for forecasting the amount of energy available via non-conventional energy sources like wind and sun so that the transition to renewable energy can be done highly efficiently without destabilizing the power grid. As the renewable power industry has abundant data that can be exploited in renewable energy forecasting, machine learning techniques can revolutionize the way we deal with renewable energy. This paper describes the efficiency of Linear Regression, Neural Networks Regression, Random Forest Regression, and Extra Tree Regression models for forecasting solar irradiation available on Earth's surface.