Non-intrusive Appliance Load Monitoring Techniques Research Articles

Privacy protection via joint real and reactive load shaping in smart grids

Frequent metering of electricity consumption is crucial for demand-side management in smart grids. However, metered data can be processed fairly easily by employing well-established nonintrusive appliance load monitoring techniques to infer appliance usage, which reveals information about consumers’ private lives. Existing load shaping techniques for privacy primarily focus only on altering metered real power, whereas smart meters collect reactive power consumption data as well for various purposes. This study addresses consumer privacy preservation via load shaping in a demand response scheme, considering both real and reactive power. We build a multi-objective optimization framework that enables us to characterize the interplay between privacy maximization, user cost minimization, and user discomfort minimization objectives. Our results reveal that minimizing information leakage due to a single component, e.g., real power, would suffer from overlooking information leakage due to the other component, e.g., reactive power, causing sub-optimal decisions. In fact, joint shaping of real and reactive power components results in the best possible privacy preservation performance, which leads to more than a twofold increase in privacy in terms of mutual information. • Impact of shaping real and reactive loads simultaneously for privacy is investigated. • A multi-objective goal programming-based optimization framework is presented. • Shaping a single load component is insufficient to attain the best possible privacy. • Jointly shaping real and reactive power enhances consumer privacy by more than 50%. • Joint load shaping for privacy increases cost and discomfort by , at most, 27%.

Holistic Privacy for Electricity, Water, and Natural Gas Metering in Next Generation Smart Homes

In smart electricity grids, high time granularity (HTG) power consumption data can be decomposed into individual appliance load signatures via nonintrusive appliance load monitoring techniques to expose appliance usage profiles. Various methods ranging from load shaping to noise addition and data aggregation have been proposed to mitigate this problem. However, with the growing scarcity of natural resources, utilities other than electricity (e.g., water and natural gas) have also begun to be subject to HTG metering, which creates privacy issues similar to that of electricity. Therefore, employing privacy protection countermeasures for only electricity usage is ineffective for appliances that utilize additional/other metered resources. As such, existing privacy countermeasures and metrics need to be reevaluated to address not only electricity, but also any other resource that is metered. Furthermore, a holistic privacy protection approach for all metered resources must be adopted as the information leak from any of the resources has the potential to render the privacy preserving countermeasures for all the other resources futile. This article introduces the privacy preservation problem for multiple HTG metered resources and explores potential solutions for its mitigation.

ELECTRICITY CONSUMPTION PATTERN DISAGGREGATION USING NON-INTRUSIVE APPLIANCE LOAD MONITORING METHOD

In practice, a standard energy meter can only capture the overall electricity consumption and estimating electricity consumption pattern of various appliances from the overall consumption pattern is complicated. Therefore, the Non-Intrusive Appliance Load Monitoring (NIALM) technique can be applied to trace electricity consumption from each appliance in a monitored building. However, the method requires a detailed, second-by-second power consumption data which is commonly not available without the use of high specification energy meter. Hence, this paper analyzes the impact of different time sampling data in estimating the energy consumption pattern of various appliances through NIALM method. This is so that consumers will have an overview of time sampling data which is required in order to apply the NIALM technique. As for the analysis, air-conditioning systems and fluorescent lamps were used in the experimental setup. One minute sample rate was the minimum time interval required by NIALM carried out in this analysis. Through the study presented in this paper, it can be established that higher time sampling led to uncertain appliance detection and low accuracy.

Low-complexity energy disaggregation using appliance load modelling

Large-scale smart metering deployments and energy saving targets across the world have ignited renewed interest in residential non-intrusive appliance load monitoring (NALM), that is, disaggregating total household’s energy consumption down to individual appliances, using purely analytical tools. Despite increased research efforts, NALM techniques that can disaggregate power loads at low sampling rates are still not accurate and/or practical enough, requiring substantial customer input and long training periods. In this paper, we address these challenges via a practical low-complexity lowrate NALM, by proposing two approaches based on a combination of the following machine learning techniques: k-means clustering and Support Vector Machine, exploiting their strengths and addressing their individual weaknesses. The first proposed supervised approach is a low-complexity method that requires very short training period and is fairly accurate even in the presence of labelling errors. The second approach relies on a database of appliance signatures that we designed using publicly available datasets. The database compactly represents over 200 appliances using statistical modelling of measured active power. Experimental results on three datasets from US, Italy, Austria and UK, demonstrate the reliability and practicality.

Identifying Appliances using NIALM with Minimum Features

Government of India has decided to install smart meters in fourteen states. Smart meters are required to identify home appliances to fulfill various tasks in the smart grid environment. Both intrusive and non-intrusive methods have been suggested for identification. However, intrusive method is not suitable for cost and privacy reasons. On the other hand, techniques using non-intrusive appliance load monitoring (NIALM) are yet to result in meaningful practical implementation. Two major challenges in NIALM research are the choice of features (load signatures of appliances), and the appropriate algorithm. Both have a direct impact on the cost of the smart meter. In this paper, we address the two issues and propose a procedure with only four features and a simple algorithm to identify appliances. Our experimental setup, on the recommended specifications of the internal electrical wiring in Indian residences, used common household appliances’ load signatures of active and reactive powers, harmonic components and their magnitudes. We show that these four features are essential and sufficient for implementation of NIALM with a simple algorithm. We have introduced a new approach of ‘multi point sensing’ and ‘group control’ rather than the ‘single point sensing’ and ‘individual control’, used so far in NIALM techniques. DOI: http://dx.doi.org/10.11591/ijece.v4i6.6715

Nonintrusive appliance load monitoring based on integer programming

AbstractThis paper presents a new nonintrusive appliance load monitoring technique based on integer programming. Nonintrusive appliance load monitoring is the problem of identifying the operating conditions of electric appliances in a house by observing only the overall load current and voltage. Since the overall load current is expressed as a superposition of the currents of the operating appliances, the monitoring problem can be formulated as an integer quadratic programming problem by expressing the operating conditions as integer variables. This problem is solvable with a sufficiently small computational burden thanks to the recent development of commercial software. The proposed method does not require relearning even when a new appliance is installed in the house. Furthermore, the proposed formulation is applicable to cases in which some appliance has multiple modes, and cases in which some appliances of the same type are operating simultaneously. The usefulness of the proposed technique is verified by experimental results. © 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 174(2): 18–25, 2011; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.21040

整数計画法を用いた電気機器の非侵入型稼働状態モニタリング

This paper presents a new nonintrusive appliance load monitoring technique based on integer programming. Nonintrusive appliance load monitoring is the problem of identifying the operating conditions of electric appliances in a house by observing only the overall load current and voltage. Since the overall load current is expressed as a superposition of the currents of the operating appliances, the monitoring problem can be formulated as an integer quadratic programming problem by expressing the operating conditions as integer variables. This problem is solvable with a sufficiently small computational burden thanks to the recent development of commercial software. The proposed method does not require relearning even when a new appliance is installed in the house. Furthermore, the proposed formulation is applicable to cases in which some appliance has multiple modes, and cases in which some appliances of the same type are operating simultaneously. The usefulness of the proposed technique is verified by experimental results. © 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 174(2): 18–25, 2011; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.21040

Residential energy monitoring and computerized surveillance via utility power flows

The author notes that all novel technologies have the potential to affect society in a complex manner, with both beneficial and detrimental consequences. He considers an illustrative case study: a nonintrusive appliance load monitoring technique that can provide vital information to help avoid future energy crises, but can also be used for surveillance purposes. He notes that there appears to be a significant potential for the technology to be abused. The danger that the technology might eventually lead to an erosion of civil liberties and privacy rights leaves its developers in an ethical quandary. The author examines how this technology should be controlled.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>