Prediction of Electricity Consumption Using Interpretable Machine Learning Approach ข้อมูลจากประเทศไทยในช่วงปี 1973 จนถึง 2021

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Published: May 31, 2025
DOI: https://doi.org/10.55164/ajstr.v28i3.257539
Keywords:
Electricity consumption machine learning macroeconomic interpretable models

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Theera Thongsanitkarn
Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand
https://orcid.org/0009-0007-0377-9426
Prompong Sugunnasil
College of Arts, Media, and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand

Abstract

The continuously increasing demand for electricity presents significant issues for policymakers and utility companies. An accurate forecast of electricity usage is critical for effective energy management. This thesis provides an interpretable machine-learning approach for forecasting energy demand that incorporates macroeconomic variables such as GDP, inflation, and industrial growth. The study uses data from the World Development Indicators from 1973 to 2021 and employs the multi-model. Our findings emphasize the importance of economic considerations on electricity demand, resulting in a reliable method for forecasting energy consumption. This study adds to the body of knowledge by providing a clear model to aid in decision-making processes connected to energy management and policy creation.

Article Details

Issue
Vol. 28 No. 3 (2025): May - June
Section
Academic Articles
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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