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Integrated Regression Approach for Prediction of Solar Irradiance Based on Multiple Weather Factors

Integrated Regression Approach for Prediction of Solar Irradiance Based on Multiple Weather Factors

Megha Kamble, Sudeshna Ghosh
Copyright: © 2021 |Volume: 11 |Issue: 2 |Pages: 12
ISSN: 2642-1577|EISSN: 2642-1585|EISBN13: 9781799864110|DOI: 10.4018/IJAIML.294105
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MLA

Kamble, Megha, and Sudeshna Ghosh. "Integrated Regression Approach for Prediction of Solar Irradiance Based on Multiple Weather Factors." IJAIML vol.11, no.2 2021: pp.1-12. http://doi.org/10.4018/IJAIML.294105

APA

Kamble, M. & Ghosh, S. (2021). Integrated Regression Approach for Prediction of Solar Irradiance Based on Multiple Weather Factors. International Journal of Artificial Intelligence and Machine Learning (IJAIML), 11(2), 1-12. http://doi.org/10.4018/IJAIML.294105

Chicago

Kamble, Megha, and Sudeshna Ghosh. "Integrated Regression Approach for Prediction of Solar Irradiance Based on Multiple Weather Factors," International Journal of Artificial Intelligence and Machine Learning (IJAIML) 11, no.2: 1-12. http://doi.org/10.4018/IJAIML.294105

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Abstract

Solar irradiance is the most vital aspect in estimating the solar energy collection at any location. Renewable energy setup at any location is dependent on it and other ambient weather parameters. However, it is hard to predict due to unstable nature and dependence on variations in weather conditions. The correlation of ambient weather factors on the performance of solar irradiance is analysed, by collecting the data using weather API, over the year for a particular location of central India. The training of this non-linear data is carried out with hybrid regression model integrating decision tree regression with Artificial Neural Network (ANN) module. Experimentation is performed using real data of different days from different seasons of the year, also by considering different irradiance conditions. The results demonstrated significant weather factors with moderate positive and negative correlation with solar irradiance, which can be used as a helpful tool to predict it before deployment of solar energy setup.