Machine Learning Models, Data Preprocessing Techniques and Suite of Metrics for Assessing Solar Power Forecasting: A Comprehensive Review
Year: 2025
Type: article
Abstract: As we know, the energy obtained from sun is intermittent in nature hence its generation is affected by rapidly changing weather conditions.Hence to balance the supply demand and to improve the accuracy and efficiency of the system, forecasting the solar energy highly necessitates.In view of this, different photovoltaic power forecasting techniques using machine learning models, different data pre-processing techniques and the evaluation metrices are discussed.Further to improve the system performance and efficiency quality data input which is preprocessed is highly required.This paper discusses the comprehensive review of different solar forecasting techniques along with traditional forecasting techniques and a comparison review of ML models, respective algorithms used and various techniques for preprocessing the data are presented.Further, different suite metrices for assessing the performance of solar power forecasting is also presented.
Authors: Asma Pes, A.M. Nagaraja
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4413888172