Research on the Influence of Hyperparameters on the LightGBM Model in Load Forecasting
Received: 30 June 2024 | Revised: 31 July 2024 | Accepted: 13 August 2024 | Online: 9 October 2024
Corresponding author: Thanh-Ngoc Tran
Abstract
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.
Keywords:
LightGBM, load forecasting, cross-validation, hyperparametersDownloads
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Copyright (c) 2024 Khanh-Toan Nguyen, Thanh-Ngoc Tran, Huy-Tuan Nguyen
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