Forecasting solar power generation with machine learning techniques
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Full Text |
Pdf
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Author |
K. Muthuvel, P. Muthukumar and Thomas Thangam
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e-ISSN |
1819-6608 |
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On Pages
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1378-1388
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Volume No. |
19
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Issue No. |
22
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Issue Date |
December 31, 2024
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DOI |
https://doi.org/10.59018/112470
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Keywords |
PV system, time-series, machine learning, XGBoost regressor, adaboost regressor.
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Abstract
This study underscores the economic and environmental advantages of integrating solar energy into power systems. The unpredictable nature of solar power poses challenges to system operation and planning. To ensure the economic sustainability of newly constructed systems, precise forecasting of Photovoltaic (PV) system effectiveness and energy output is crucial. Addressing variations in solar power consumption, this work presents an enhanced Machine Learning (ML) model. Utilizing Python, the study explores Linear Regressor, Random Forest (RF) Regressor, XGBoost Regression, K-Nearest Neighbor (KNN) Regressor, and AdaBoost Regressor approaches, all proving effective in predicting electricity production. Results highlight the superior performance of ML algorithms over traditional time series methods and two baseline models, emphasizing their efficacy in solar power forecasting.
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