Research on the Influence of Hyperparameters on the LightGBM Model in Load Forecasting

Electric load forecasting plays a vital role in all aspects of the electrical system, including generation, transmission, distribution, and electricity retail. The LightGBM ensemble learning method has been widely applied in load forecasting and has yielded many positive results. This study presents...

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Bibliographic Details
Published in:Engineering, technology & applied science research technology & applied science research, 2024-10, Vol.14 (5), p.17005-17010
Main Authors: Nguyen, Khanh-Toan, Tran, Thanh-Ngoc, Nguyen, Huy-Tuan
Format: Article
Language:English
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Summary:Electric load forecasting plays a vital role in all aspects of the electrical system, including generation, transmission, distribution, and electricity retail. The LightGBM ensemble learning method has been widely applied in load forecasting and has yielded many positive results. This study presents an algorithm combining the grid space of hyperparameters with cross-validation to evaluate the accuracy of LightGBM models across different hyperparameter values. Peak load data from Ho Chi Minh City were used to enhance the reliability of the results. Analysis of the results based on boxplot statistical charts indicated that the accuracy of the LightGBM model significantly depends on the hyperparameter values. Moreover, using default hyperparameter values may result in large errors in load forecasting.
ISSN:2241-4487
1792-8036
DOI:10.48084/etasr.8266