کنفرانس بین المللی فناوری و مدیریت انرژی

صفحه اصلی / دهمین کنفرانس بین المللی فناوری و مدیریت انرژی

Comparative Evaluation of Machine Learning Methods for Predicting Energy Consumption in Buildings

نویسندگان :

Mahmood Seyyedzadeh¹ Pooya Zeynali² Alireza Tajdid³ Amir A. ghavifekr⁴ Reza Behinfaraz⁵

1- University of Tabriz 2- University of Tabriz 3- University of Tabriz 4- University of Tabriz 5- urmia university

کلمات کلیدی :

Building Energy Consumption،Energy Efficiency،Machine Learning،Forecasting

چکیده :

Accurate forecasting of building energy consumption is vital for sustainable energy management. In this study, we propose a machine learning framework that leverages an extensive dataset from over one hundred Southern California buildings (January 2018–January 2024) comprising hourly measurements of energy usage, environmental conditions, and operational metrics. By comparing a Decision Tree model with ensemble methods—including Random Forest, Gradient Boosting, XGBoost, and LightGBM—we demonstrate that ensemble approaches achieve superior predictive performance (R² > 0.95) and effectively capture seasonal and operational variations. These findings highlight the potential of data-driven techniques in optimizing energy management and informing targeted efficiency interventions in the built environment.

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ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 44.5.0