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Augmenting an Assisted Living Lab with Non-Intrusive Load Monitoring (2002.05593v1)

Published 13 Feb 2020 in eess.SP and cs.HC

Abstract: The need for reducing our energy consumption footprint and the increasing number of electric devices in today's homes is calling for new solutions that allow users to efficiently manage their energy consumption. Real-time feedback at device level would be of a significant benefit for this application. In addition, the aging population and their wish to be more autonomous have motivated the use of this same real-time data to indirectly monitor the household's occupants for their safety. By breaking down aggregate power consumption into its components, Non-Intrusive Load Monitoring provides information on individual appliances and their current state of operation. Since no additional metering equipment is required, residents are not confronted with intrusion into their familiar environment. Our work aims to depict an architecture supporting non-intrusive measurement with a smart electricity meter and the handling of these data using an open-source platform that allows to visualize and process real-time data about the total energy consumed. As a case study, we describe a series of measurements from common household devices and show how abnormal behavior can be detected.

Citations (15)

Summary

  • The paper's main contribution is showing that NILM data can effectively disaggregate appliance usage to enhance energy management in assisted living environments.
  • Methodologically, the study integrates smart meters with open-source platforms and MySQL databases for real-time energy consumption analysis and anomaly detection.
  • The implications include significant energy savings and improved resident safety through early detection of abnormal appliance behavior in assisted living scenarios.

Augmenting Assisted Living Environments with Non-Intrusive Load Monitoring

The paper "Augmenting an Assisted Living Lab with Non-Intrusive Load Monitoring" explores a pragmatic approach to integrating Non-Intrusive Load Monitoring (NILM) into assisted living environments, enhancing both energy efficiency and occupant safety. The research primarily focuses on the confluence of NILM and Ambient Assisted Living (AAL) systems, aiming to provide real-time device-level feedback without compromising the residents' environment with invasive sensors.

Overview

The authors emphasize the dual necessity in contemporary residential settings: the reduction of energy consumption and the enhancement of autonomous living conditions for an aging population. NILM emerges as a viable solution, capable of disaggregating aggregate power consumption data to provide insights into individual appliance usage without the need for additional intrusive monitoring equipment. This approach leverages the existing smart electricity infrastructure, offering an economically feasible option for large-scale deployment.

System Architecture and Implementation

The proposed architecture features a smart meter integrated with an open-source platform, openHAB, for the real-time visualization and processing of energy data. This setup facilitates the monitoring and management of energy usage in a non-intrusive manner. The smart meter's data is relayed to a MySQL database, which serves as a backend for storing detailed power consumption data, enabling NILM algorithms to analyze and deconstruct these data into identifiable appliance events.

Case Studies and Findings

Two illustrative case studies underscore the utility of the proposed system in detecting both typical usage patterns and anomalies. The first paper involves a household scenario where various common appliances are monitored, demonstrating how NILM data can elucidate specific appliance events. This data is crucial for formulating energy-saving strategies and identifying high-consumption devices.

The second case paper focuses on anomaly detection, a feature vital for AAL. By simulating a situation where a refrigerator is left open, the paper highlights NILM's capability in recognizing deviations from normal appliance behavior. Such insights are invaluable in an AAL setting where timely interventions can prevent potential hazards or offer healthcare insights.

Implications and Future Directions

The implications of this integration are profound. Practically, this system could significantly reduce energy consumption in residential environments by informing occupants about their usage patterns. Theoretically, it sets a precedent for further research in minimizing the sensor footprint while maximizing data yield and fidelity.

For future research, the authors suggest enhancing NILM algorithm accuracy, perhaps by integrating additional types of sensors where non-electric features are insufficiently captured by current NILM systems. Moreover, the paper envisions advancements in NILM technology that could lead to better behavioral analyses and the development of responsive AAL systems, capable of proactively alerting residents or caregivers to unusual activities — a critical feature for aging-in-place strategies.

In conclusion, the paper provides a solid framework showing that integrating NILM with open-source platforms can yield significant benefits in energy management and assisted living environments. As NILM technologies mature, their role in forming the backbone of intelligent home environments is likely to expand, offering new possibilities for efficient and safe living solutions.

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