A data stream model for home device description

Mahrsi, Mohamed Khalil El; Vignes, Sylvie; Hébrail, Georges; Picard, Marie-Luce

doi:10.1109/rcis.2009.5089303

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…However, a huge concern is that smart meters threaten consumers' privacy as data mining techniques are applied to energy consumption traces in order to infer consumers' habits and behaviors [2,23,24]. This information may be used for other purposes besides improving the efficiency of smart grids, thereby giving rise to privacy concerns [25].…”

Section: Discussionmentioning

confidence: 99%

Incentive-compatible demand-side management for smart grids based on review strategies

Schaar

2015

EURASIP J. Adv. Signal Process.

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Demand-side load management is able to significantly improve the energy efficiency of smart grids. Since the electricity production cost depends on the aggregate energy usage of multiple consumers, an important incentive problem emerges: self-interested consumers want to increase their own utilities by consuming more than the socially optimal amount of energy during peak hours since the increased cost is shared among the entire set of consumers. To incentivize self-interested consumers to take the socially optimal scheduling actions, we design a new class of protocols based on review strategies. These strategies work as follows: first, a review stage takes place in which a statistical test is performed based on the daily prices of the previous billing cycle to determine whether or not the other consumers schedule their electricity loads in a socially optimal way. If the test fails, the consumers trigger a punishment phase in which, for a certain time, they adjust their energy scheduling in such a way that everybody in the consumer set is punished due to an increased price. Using a carefully designed protocol based on such review strategies, consumers then have incentives to take the socially optimal load scheduling to avoid entering this punishment phase. We rigorously characterize the impact of deploying protocols based on review strategies on the system's as well as the users' performance and determine the optimal design (optimal billing cycle, punishment length, etc.) for various smart grid deployment scenarios. Even though this paper considers a simplified smart grid model, our analysis provides important and useful insights for designing incentive-compatible demand-side management schemes based on aggregate energy usage information in a variety of practical scenarios.

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Section: Discussionmentioning

confidence: 99%

Incentive-compatible demand-side management for smart grids based on review strategies

Schaar

2015

EURASIP J. Adv. Signal Process.

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“…Secondly, clustering techniques are applied to the power levels, to minimize the effects of noisy power measurements, as well as to ascertain which power levels are actually relevant. This approach could be implemented massively at the level of the electricity utility by using data stream models, such as the one proposed in Reference [35].…”

Section: Data Overviewmentioning

confidence: 99%

Non-Intrusive Load Monitoring (NILM) for Energy Disaggregation Using Soft Computing Techniques

et al. 2020

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Non-intrusive load monitoring (NILM) has become an important subject of study, since it provides benefits to both consumers and utility companies. The analysis of smart meter signals is useful for identifying consumption patterns and user behaviors, in order to make predictions and optimizations to anticipate the use of electrical appliances at home. However, the problem with this kind of analysis rests in how to isolate individual appliances from an aggregated consumption signal. In this work, we propose an unsupervised disaggregation method based on a controlled dataset obtained using smart meters in a standard household. By using soft computing techniques, the proposed methodology can identify the behavior of each of the devices from aggregated consumption records. In the approach developed in this work, it is possible to detect changes in power levels and to build a box model, consisting of a sequence of rectangles of different heights (power) and widths (time), which is highly adaptable to the real-life working conditions of household appliances. The system was developed and tested using data collected at households in France and the UK (UK-domestic appliance-level electricity (DALE) dataset). The proposed analysis method serves as a basis to be applied to large amounts of data collected by distribution companies with smart meters.

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