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English

صفحه اصلی / دومین کنفرانس ملی عصر انفجار تکنولوژی؛ هوش مصنوعی، تحولی در صنعت، تجارت و زنجیره تامین و دومین کنفرانس ملی علم داده در کاربردهای مهندسی

Modifying the electricity consumption pattern by designing an intelligent machine learning model with the XG Boost algorithm

نویسندگان :

Raha Pakzad¹

1- دانشگاه صنعتی ارومیه

کلمات کلیدی :

Smart Metering،Artificial Intelligence،Consumption Patterns،Energy Efficiency،Machine Learning

چکیده :

The growing integration of renewable energy sources and the increasing complexity of modern power grids have underscored the importance of smart metering in efficient energy management. Traditionally limited to basic data collection, smart meters have evolved significantly with the advent of Artificial Intelligence (AI), particularly machine learning (ML) and deep learning (DL) techniques. These technologies enable advanced analytics for identifying energy consumption patterns, predicting demand, detecting anomalies, and influencing user behavior. This paper presents a comprehensive review and empirical analysis of AI-driven smart metering applications across residential, commercial, and industrial domains. Through a multi-phase methodology, we examine how AI models contribute to forecasting accuracy, operational efficiency, and enhanced demand response strategies. Furthermore, we explore the role of behavioral analytics in shaping consumer habits and supporting energy conservation. The study offers practical insights for utilities, grid operators, and policymakers, emphasizing the critical role of intelligent metering in the transition toward more sustainable and adaptive energy systems.