A Framework based on Load Monitoring and Association Rules Mining for Pattern Recognition in Smart Homes

Mahan Ahmadi Rahmatabadi(1), Hamid Reza Pilehvar Javid(2), Zahra Dehghani Arani(2), Hamid Reza Baghaee(3), Gevork B. Gharehpetian(2) 1. Department of Energy Engineering and Physics, Amirkabir University of Technology (Tehran Polytechnic),Tehran, Iran. 2. Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. 3. Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran, 2026-06-27

Abstract

This paper presents an integrated analytical framework for residential energy management that combines appliance-level load monitoring with behavioral pattern discovery using data from a unified smart home deployment. The research employs a two-phase methodology on synchronized datasets collected from the same residential environment. First, a Random Forest classifier is trained to identify individual household appliances from aggregate electrical measurements, achieving highly accurate classification performance for eight appliance types. Then, appliance activation records are analyzed within a given time window using Association Rule Mining, which reveals significant co-occurrence patterns. This framework presents how synchronized load monitoring, combined with data mining, can offer holistic information for optimizing energy consumption, demand response programs, as well as power quality assessment, especially in environments that incorporate renewable energy sources.

Key words: Association Rule Mining, Load Monitoring, Machine Learning, Smart Home

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