Work

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

Year: 2024

Type: article

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.

Source: Engineering Technology & Applied Science Research

DOI: 10.48084/etasr.8266

Authors: Khanh-Toan Nguyen, Thanh-Ngoc Tran, Huy-Tuan Nguyen

Institution: Industrial University of Ho Chi Minh City

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $431