Weather-Driven Energy Consumption Modeling of the Laman Hikmah Library Utilizing Machine Learning
Received: 30 September 2025 | Revised: 6 November 2025 | Accepted: 17 November 2025 | Online: 30 March 2026
Corresponding author: Mohamad Fani Sulaima
Abstract
The objective of this study is to analyze the relationship between weather variables and daily energy usage at the Laman Hikmah Library (LHL) at Universiti Teknikal Malaysia Melaka (UTeM) using Least Squares Support Vector Machine (LSSVM) and Support Vector Machine (SVM). The findings indicate that LSSVM achieved R² = 0.65 and RMSE = 747 kWh, outperforming the SVM with the temperature, humidity, and pressure emerging as dominant predictors. This study provides empirical evidence for climate-responsive energy modelling in tropical regions and demonstrates the value of advanced machine learning in supporting Malaysia’s energy transition agenda. By aligning with the United Nations Sustainable Development Goals, this study contributes to both national policy frameworks and global sustainability targets.
Keywords:
energy consumption, weather data, energy modeling, machine learningDownloads
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Copyright (c) 2026 Norhafiza Mohamad, Mohamad Fani Sulaima, Rohaida Hussain, Nor Afiza Mohd Noor, Agileswari Ramasamy, Nezihe Ayas
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