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Machine Learning Models, Data Preprocessing Techniques and Suite of Metrics for Assessing Solar Power Forecasting: A Comprehensive Review

Asma A.M. Nagaraja
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Machine Learning Models, Data Preprocessing Techniques and Suite of Metrics for Assessing Solar Power Forecasting: A Comprehensive Review

by Asma, A.M. Nagaraja
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Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 8 - Number 1
Year of Publication: 2025
Authors: Asma, A.M. Nagaraja
10.5120/cae2025652911

Asma, A.M. Nagaraja . Machine Learning Models, Data Preprocessing Techniques and Suite of Metrics for Assessing Solar Power Forecasting: A Comprehensive Review. Communications on Applied Electronics. 8, 1 ( Aug 2025), 8-20. DOI=10.5120/cae2025652911

@article{ 10.5120/cae2025652911,
author = { Asma, A.M. Nagaraja },
title = { Machine Learning Models, Data Preprocessing Techniques and Suite of Metrics for Assessing Solar Power Forecasting: A Comprehensive Review },
journal = { Communications on Applied Electronics },
issue_date = { Aug 2025 },
volume = { 8 },
number = { 1 },
month = { Aug },
year = { 2025 },
issn = { 2394-4714 },
pages = { 8-20 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume8/number1/machine-learning-models-data-preprocessing-techniques-and-suite-of-metrics-for-assessing-solar-power-forecasting-a-comprehensive-review/ },
doi = { 10.5120/cae2025652911 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-08-31T03:09:03+05:30
%A Asma
%A A.M. Nagaraja
%T Machine Learning Models, Data Preprocessing Techniques and Suite of Metrics for Assessing Solar Power Forecasting: A Comprehensive Review
%J Communications on Applied Electronics
%@ 2394-4714
%V 8
%N 1
%P 8-20
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
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 pre-processed 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.

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Index Terms

Computer Science
Information Sciences

Keywords

Photovoltaic Power forecasting Data pre-processing Machine learning Metrics

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