Solar Energy Forecasting: Perspectives of the State-Of-The-Art

¹ P.M. Thevar College, Usilampatti. Tamilnadu, India
^* Department of Civil Engineering, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun - 248007, India
^† Department of Electrical & Electronics Engineering, IES College Of Technology, IES University, Madhya Pradesh 462044 India
^‡ The Islamic university, Iraq
^§ Assistant Professor, Department of IT, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai - 127, Najaf
⁶ Assistant Professor, Department of BCA, K.S.Rangasamy College of Arts and Science (Autonomous), Tiruchengode. Mail Id: jeevinand@gmail.com

^* Corresponding Author: ashispathani91@gmail.com
^† research@iesbpl.ac.in
^‡ muntatheralmusawi@gmail.com
^§ allirani.p_cse@psvpec.in

Abstract

Solar energy is a promising renewable energy source, but its intermittent and variable nature poses significant challenges for accurate forecasting. Over the recent years, there has been a remarkable surge in research dedicated to improving the precision of solar energy forecasting models. This review article delves into the state-of-the-art in solar energy forecasting. Beginning with an exploration of the hurdles faced in forecasting solar radiation, we proceed to provide an extensive survey of various forecasting models that have been developed to tackle this complex problem. Factors influencing the accuracy of solar energy forecasts are discussed, and an insight into the future trends in solar energy forecasting is provided. Key areas of focus include machine learning techniques, artificial neural networks (ANNs), and support vector regression.