A Bayesian Optimization-Based AutoML Framework for Non-Intrusive Load Monitoring

2026-02-05

AI Summary

This work addresses the bottleneck in non-intrusive load monitoring (NILM) caused by reliance on expert knowledge for model selection and hyperparameter tuning by systematically introducing Bayesian optimization-based automated machine learning (AutoML) into energy disaggregation. The proposed AutoML4NILM framework integrates eleven mainstream NILM algorithms along with their tunable hyperparameters, offering flexible extensibility and automating both model selection and hyperparameter optimization through Bayesian optimization. As an open-source toolkit, AutoML4NILM substantially lowers the barrier to NILM modeling, enabling non-expert users to efficiently deploy high-performance disaggregation solutions while simultaneously improving both modeling efficiency and accuracy.

Technology Category

• Machine Learning: Auto ML and Hyperparameter Tuning
• Natural Language Processing: Learning & Optimization for NLP
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Non-Intrusive Load Monitoring (NILM), commonly known as energy disaggregation, aims to estimate the power consumption of individual appliances by analyzing a home's total electricity usage. This method provides a cost-effective alternative to installing dedicated smart meters for each appliance. In this paper, we introduce a novel framework that incorporates Automated Machine Learning (AutoML) into the NILM domain, utilizing Bayesian Optimization for automated model selection and hyperparameter tuning. This framework empowers domain practitioners to effectively apply machine learning techniques without requiring advanced expertise in data science or machine learning. To support further research and industry adoption, we present AutoML4NILM, a flexible and extensible open-source toolkit designed to streamline the deployment of AutoML solutions for energy disaggregation. Currently, this framework supports 11 algorithms, each with different hyperparameters; however, its flexible design allows for the extension of both the algorithms and their hyperparameters.

Problem

• Non-Intrusive Load Monitoring
• AutoML
• Bayesian Optimization
• Energy Disaggregation
• Hyperparameter Tuning

Innovation

• Bayesian Optimization
• AutoML
• Non-Intrusive Load Monitoring
• Energy Disaggregation
• Hyperparameter Tuning