Performance Comparison of Random Forest Regressor and Support Vector Regression for Solar Energy Prediction

Abstract This study conducts a comparative analysis between the Random Forest Regressor (RFR) and Support Vector Regression (SVR) for solar energy prediction. Solar energy, a prominent renewable energy source, but its predictability is challenging due to changing weather conditions. Micro grid opera...

Full description

Saved in:
Bibliographic Details
Published inIOP conference series. Earth and environmental science Vol. 1375; no. 1; pp. 12013 - 12018
Main Authors Lakshmi Yerrabolu, Venkata, Kasireddy, Idamakanti, Jasmine, K M, Murali Krishna Vamsi, T B, Joshua, N, Shyam Kumar, V, Rao, DSNM
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.07.2024
Subjects
Cloud cover
Comparative analysis
Dew point
Distributed generation
Machine learning
Meteorological data
Performance prediction
Prediction models
Regression
Regression analysis
Renewable energy sources
Solar energy
Support vector machines
Weather
Online AccessGet full text

Cover

More Information
Summary:Abstract This study conducts a comparative analysis between the Random Forest Regressor (RFR) and Support Vector Regression (SVR) for solar energy prediction. Solar energy, a prominent renewable energy source, but its predictability is challenging due to changing weather conditions. Micro grid operators, responsible for managing smaller, localized energy systems, often struggle to balance supply and demand efficiently because of the unpredictable nature of solar energy. To tackle this issue, we use machine learning models, RFR and SVR, with historical weather data including dew point, temperature, cloud cover, visibility and wind. Outcomes focus the superior performance of the Random Forest Regressor process compared to the Support Vector Regression in forecasting solar energy, demonstrating its potential for enhancing reliability in solar energy prediction models.
ISSN:1755-1307
1755-1315
DOI:10.1088/1755-1315/1375/1/012013