Energy Disaggregation Using Elastic Matching Algorithms

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)In this article an energy disaggregation architecture using elastic matching algorithms is presented. The architecture uses a database of reference energy consumption signatures and compares them with incoming energy consumption frames using template matching. In contrast to machine learning-based approaches which require significant amount of data to train a model, elastic matching-based approaches do not have a model training process but perform recognition using template matching. Five different elastic matching algorithms were evaluated across different datasets and the experimental results showed that the minimum variance matching algorithm outperforms all other evaluated matching algorithms. The best performing minimum variance matching algorithm improved the energy disaggregation accuracy by 2.7% when compared to the baseline dynamic time warping algorithm.Peer reviewedFinal Published versio

Reducing Cascading Failure Risk by Increasing Infrastructure Network Interdependency

Increased coupling between critical infrastructure networks, such as power and communication systems, will have important implications for the reliability and security of these systems. To understand the effects of power-communication coupling, several have studied interdependent network models and reported that increased coupling can increase system vulnerability. However, these results come from models that have substantially different mechanisms of cascading, relative to those found in actual power and communication networks. This paper reports on two sets of experiments that compare the network vulnerability implications resulting from simple topological models and models that more accurately capture the dynamics of cascading in power systems. First, we compare a simple model of topological contagion to a model of cascading in power systems and find that the power grid shows a much higher level of vulnerability, relative to the contagion model. Second, we compare a model of topological cascades in coupled networks to three different physics-based models of power grids coupled to communication networks. Again, the more accurate models suggest very different conclusions. In all but the most extreme case, the physics-based power grid models indicate that increased power-communication coupling decreases vulnerability. This is opposite from what one would conclude from the coupled topological model, in which zero coupling is optimal. Finally, an extreme case in which communication failures immediately cause grid failures, suggests that if systems are poorly designed, increased coupling can be harmful. Together these results suggest design strategies for reducing the risk of cascades in interdependent infrastructure systems

Architectural design and load flow study of power flow routers

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With the large-scale roll-out of smart metering worldwide, there is a growing need to account for the individual contribution of appliances to the load demand. In this paper, we design a Graph signal processing (GSP)-based approach for non-intrusive appliance load monitoring (NILM), i.e., disaggregation of total energy consumption down to individual appliances used. Leveraging piecewise smoothness of the power load signal, two GSP-based NILM approaches are proposed. The first approach, based on total graph variation minimization, searches for a smooth graph signal under known label constraints. The second approach uses the total graph variation minimizer as a starting point for further refinement via simulated annealing. The proposed GSP-based NILM approach aims to address the large training overhead and associated complexity of conventional graph-based methods through a novel event-based graph approach. Simulation results using two datasets of real house measurements demonstrate the competitive performance of the GSP-based approaches with respect to traditionally used Hidden Markov Model-based and Decision Tree-based approaches

Reliable and Efficient Access for Alarm-initiated and Regular M2M Traffic in IEEE 802.11ah Systems

EEE 802.11ah is a novel WiFi-based protocol, aiming to provide an access solution for the machine-to-machine (M2M) communications. In this paper, we propose an adaptive access mechanism that can be seamlessly incorporated into IEEE 802.11ah protocol operation and that supports all potential M2M reporting regimes, which are periodic, on-demand We show that it is possible to both efficiently and reliably resolve all reporting stations in the cell, within the limits of the allowed deadlines. As a side result, we also provide a rationale for modeling the inter-arrival time in alarm events by using the Beta distribution, a model that is considered in the 3GPP standardization.Comment: Appeared in IEEE IoT Journal, October 201

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Activities are a descriptive term for the common ways households spend their time. Examples include daily routines such as cooking, doing laundry, and Computing. Smart energy meter data can be used to generate time profiles of activities that are meaningful to households’ own lived experience. Activities are therefore a lens through which energy feedback to households can be made salient and understandable. This paper demonstrates how hourly time profiles of household activities can be inferred from smart energy meter data, supplemented by appliance monitors and environmental sensors. In-depth interviews and home surveys are used to identify appliances and devices used for a range of activities. These relationships between te chnologies and activities are captured in an ‘activity ontology’ that can be applied to smart meter data to make inferences on hourly time profiles of up to nine everyday activities. Results are presented from six homes participating in a UK trial of smart home technologies. The duration of activities and when they are carried out is examined within households. The time profile of domestic activities has routine characteristics but these tend to vary widely between households with different socio-demo graphic characteristics. Analysing the energy consumption associated with different activities leads to a useful means of providing activity-itemised energy feedback, and also reveals certain households to be high energy-using across a range of activities