Activity Detection And Modeling Using Smart Meter Data: Concept And Case Studies

TL;DR

This paper introduces a novel approach to activity disaggregation using smart meter data, leveraging machine learning to detect and model consumer activities, with potential applications in energy management and behavioral analysis.

Contribution

It proposes a new activity disaggregation framework based on load data, demonstrating its effectiveness and discussing its advantages over traditional appliance load disaggregation.

Findings

Effective activity detection demonstrated through case studies

Time-dependent activity modeling reveals consumer behavior patterns

Potential applications in energy efficiency and demand response

Abstract

Electricity consumed by residential consumers counts for a significant part of global electricity consumption and utility companies can collect high-resolution load data thanks to the widely deployed advanced metering infrastructure. There has been a growing research interest toward appliance load disaggregation via nonintrusive load monitoring. As the electricity consumption of appliances is directly associated with the activities of consumers, this paper proposes a new and more effective approach, i.e., activity disaggregation. We present the concept of activity disaggregation and discuss its advantage over traditional appliance load disaggregation. We develop a framework by leverage machine learning for activity detection based on residential load data and features. We show through numerical case studies to demonstrate the effectiveness of the activity detection method and analyze consumer behaviors by time-dependent activity modeling. Last but not least, we discuss some potential use cases that can benefit from activity disaggregation and some future research directions.