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دهمین کنفرانس بین المللی فناوری و مدیریت انرژی
Comparative Evaluation of Machine Learning Methods for Predicting Energy Consumption in Buildings
نویسندگان :
Mahmood Seyyedzadeh
1
Pooya Zeynali
2
Alireza Tajdid
3
Amir A. ghavifekr
4
Reza Behinfaraz
5
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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بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 44.8.1
