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Proposing a Hybrid Genetic Algorithm based Parsimonious Random Forest Regression (H-GAPRFR) technique for solar irradiance forecasting with feature selection and parameter optimization

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Abstract

Machine learning has sparked a wide set of solar prediction experiments due to its recent success, which is one of the most common solutions for solar irradiance forecasting problems specifically. However, while using machine learning regression algorithms, additional attention must be paid to feature selection as well as effective parameter optimization. As a result, this work provides a parsimonious based genetic algorithm that incorporates feature selection integrated with random forest regression. The impact of wind speed on the solar irradiance problem has also been investigated. The performance of the proposed H-GAPRFR algorithm is tested for the location Madurai, India, with eight meteorological data variables over a year and has been validated through statistical metrics such as RMSE, MAE, and coefficient of Determination. In comparison to conventional Support Vector Regression (SVR) and Random Forest (RF) regression techniques, the suggested H-GAPRFR model reduced RMSE by 64.18% and 7.43%, respectively. Second, the suggested H-GAPRFR algorithm was used to investigate the effect of wind speed. From the analysis, it is clear that the proposed model considering wind speed improves the prediction accuracy by further reducing RMSE and MAE by 10.2% and 6.5%, respectively. As a result, it is indicated that in the problem of solar irradiance estimation, the parsimonious model with feature selection can produce improved prediction accuracy.

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Abbreviations

ANN:

Artificial Neural Network

ARMA:

Autoregressive Moving Average model

CV:

Cross Validation

GAP-RFR:

GA Parsimonious Random Forest

GAP-SVR:

GA Parsimonious Support Vector Regression

GHI:

Global Horizontal Irradiation

GIS:

Geographic Information Systems

GW:

Gigawatts

MAE:

Mean Absolute Error

MAPE:

Mean Absolute Percentage Error

PV:

Photovoltaic

R2:

Coefficient of Determination

RBF:

Radial Basis Function

RF:

Random Forest

RMSE:

Root Mean Square Error

SVM:

Support Vector Machine

SVR:

Support Vector Regression

TWh:

Terawatt-hours

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, 625015, Madurai, Tamilnadu, India

    Josalin Jemima J., Nelson Jayakumar D., Charles Raja S. & Venkatesh P.

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  1. Josalin Jemima J.
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  2. Nelson Jayakumar D.
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J., J., D., N., S., C. et al. Proposing a Hybrid Genetic Algorithm based Parsimonious Random Forest Regression (H-GAPRFR) technique for solar irradiance forecasting with feature selection and parameter optimization. Earth Sci Inform 15, 1925–1942 (2022). https://doi.org/10.1007/s12145-022-00839-y

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