Privacy-Utility Tradeoff for Applications Using Energy Disaggregation of Smart-Meter Data

Smart meters are used to measure the energy consumption of households. Specifically, within the energy consumption task, a smart meter must be used for load forecasting, the reduction in consumer bills as well as the reduction in grid distortions. Smart meters can be used to disaggregate the energy consumption at the device level. In this paper, we investigated the potential of identifying the multimedia content played by a TV or monitor device using the central house’s smart meter measuring the aggregated energy consumption from all working appliances of the household. The proposed architecture was based on the elastic matching of aggregated energy signal frames with 20 reference TV channel signals. Different elastic matching algorithms, which use symmetric distance measures, were used with the best achieved video content identification accuracy of 93.6% using the MVM algorithm.

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Evaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter

Algorithms ◽

10.3390/a14110311 ◽

2021 ◽

Vol 14 (11) ◽

pp. 311

Author(s):

Christina Koutroumpina ◽

Spyros Sioutas ◽

Stelios Koutroubinas ◽

Kostas Tsichlas

Keyword(s):

Low Cost ◽

Sampling Rate ◽

Classification Performance ◽

General Purpose ◽

Supervised Machine Learning ◽

Smart Meter ◽

Decision Tree Classifier ◽

Household Level ◽

Tree Classifier ◽

Energy Disaggregation

The problem of energy disaggregation is the separation of an aggregate energy signal into the consumption of individual appliances in a household. This is useful, since the goal of energy efficiency at the household level can be achieved through energy-saving policies towards changing the behavior of the consumers. This requires as a prerequisite to be able to measure the energy consumption at the appliance level. The purpose of this study is to present some initial results towards this goal by making heavy use of the characteristics of a particular din-rail meter, which is provided by Meazon S.A. Our thinking is that meter-specific energy disaggregation solutions may yield better results than general-purpose methods, especially for sophisticated meters. This meter has a 50 Hz sampling rate over 3 different lines and provides a rather rich set of measurements with respect to the extracted features. In this paper we aim at evaluating the set of features generated by the smart meter. To this end, we use well-known supervised machine learning models and test their effectiveness on certain appliances when selecting specific subsets of features. Three algorithms are used for this purpose: the Decision Tree Classifier, the Random Forest Classifier, and the Multilayer Perceptron Classifier. Our experimental study shows that by using a specific set of features one can enhance the classification performance of these algorithms.

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An optimisation-based energy disaggregation algorithm for low frequency smart meter data

Energy Informatics ◽

10.1186/s42162-019-0089-8 ◽

2019 ◽

Vol 2 (S1) ◽

Cited By ~ 2

Author(s):

Cristina Rottondi ◽

Marco Derboni ◽

Dario Piga ◽

Andrea Emilio Rizzoli

Keyword(s):

Energy Consumption ◽

State Of The Art ◽

Low Frequency ◽

Smart Meter ◽

Washing Machine ◽

Total Energy Consumption ◽

Energy Disaggregation ◽

Load Monitoring ◽

Individual Contributions ◽

The Individual

Abstract An algorithm for the non-intrusive disaggregation of energy consumption into its end-uses, also known as non-intrusive appliance load monitoring (NIALM), is presented. The algorithm solves an optimisation problem where the objective is to minimise the error between the total energy consumption and the sum of the individual contributions of each appliance. The algorithm assumes that a fraction of the loads present in the household is known (e.g. washing machine, dishwasher, etc.), but it also considers unknown loads, treating them as a single load. The performance of the algorithm is then compared to that obtained by two state of the art disaggregation approaches implemented in the publicly available NILMTK framework. The first one is based on Combinatorial Optimization, the second one on a Factorial Hidden Markov Model. The results show that the proposed algorithm performs satisfactorily and it even outperforms the other algorithms from some perspectives.

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