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Comparative Analysis Using Multiple Regression Models for Forecasting Photovoltaic Power Generation

Author

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  • Burhan U Din Abdullah

    (School of Engineering and Technology, Sharda University, Knowledge Park III, Greater Noida 201306, Uttar Pradesh, India)

  • Shahbaz Ahmad Khanday

    (School of Engineering and Technology, Sharda University, Knowledge Park III, Greater Noida 201306, Uttar Pradesh, India)

  • Nair Ul Islam

    (School of Engineering and Technology, Sharda University, Knowledge Park III, Greater Noida 201306, Uttar Pradesh, India)

  • Suman Lata

    (School of Engineering and Technology, Sharda University, Knowledge Park III, Greater Noida 201306, Uttar Pradesh, India)

  • Hoor Fatima

    (School of Engineering and Technology, Sharda University, Knowledge Park III, Greater Noida 201306, Uttar Pradesh, India)

  • Sarvar Hussain Nengroo

    (The Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology, Daejeon 34051, Republic of Korea
    Department of Engineering and Technology, Technical University of Denmark (DTU), 2800 Ballerup, Denmark)

Abstract

Effective machine learning regression models are useful toolsets for managing and planning energy in PV grid-connected systems. Machine learning regression models, however, have been crucial in the analysis, forecasting, and prediction of numerous parameters that support the efficient management of the production and distribution of green energy. This article proposes multiple regression models for power prediction using the Sharda University PV dataset (2022 Edition). The proposed regression model is inspired by a unique data pre-processing technique for forecasting PV power generation. Performance metrics, namely mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), R 2 -score, and predicted vs. actual value plots, have been used to compare the performance of the different regression. Simulation results show that the multilayer perceptron regressor outperforms the other algorithms, with an RMSE of 17.870 and an R 2 score of 0.9377. Feature importance analysis has been performed to determine the most significant features that influence PV power generation.

Suggested Citation

  • Burhan U Din Abdullah & Shahbaz Ahmad Khanday & Nair Ul Islam & Suman Lata & Hoor Fatima & Sarvar Hussain Nengroo, 2024. "Comparative Analysis Using Multiple Regression Models for Forecasting Photovoltaic Power Generation," Energies, MDPI, vol. 17(7), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1564-:d:1363518
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    References listed on IDEAS

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    1. Huang, Jing & Perry, Matthew, 2016. "A semi-empirical approach using gradient boosting and k-nearest neighbors regression for GEFCom2014 probabilistic solar power forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1081-1086.
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