A Hybrid Machine Learning Model for Solar Power Forecasting

¹ New Prince Shri Bhavani College Of Engineering and Technology, Approved by AICTE, Affilated To Anna University
² Bannari Amman Institute of Technology, Erode, India
³ Professor, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai - 127
⁴ Assistant Professor, Prince Dr. K. Vasudevan College of Engineering and Technology, Chennai – 127

^* Correspondingauthor: prakashk@bitsathy.ac.in

Abstract

The paper presents a near investigation of different AI procedures for solar power forecasting. The objective of the research is to identify the most accurate and efficient machine learning algorithms for solar power forecasting. The paper also considers different parameters such as weather conditions, solar radiation, and time of day in the forecasting model. This paper proposes a hybrid machine learning model for solar power forecasting that consolidates the strengths of multiple algorithms, including support vector regression, random forest regression, and artificial neural network. However, the study also highlights the importance of incorporating domain knowledge and feature engineering in machine learning models for better forecasting accuracy.