Load Hiding of Household's Power Demand

With the development and introduction of smart metering, the energy information for costumers will change from infrequent manual meter readings to fine-grained energy consumption data. On the one hand these fine-grained measurements will lead to an improvement in costumers' energy habits, but on the other hand the fined-grained data produces information about a household and also households' inhabitants, which are the basis for many future privacy issues. To ensure household privacy and smart meter information owned by the household inhabitants, load hiding techniques were introduced to obfuscate the load demand visible at the household energy meter. In this work, a state-of-the-art battery-based load hiding (BLH) technique, which uses a controllable battery to disguise the power consumption and a novel load hiding technique called load-based load hiding (LLH) are presented. An LLH system uses an controllable household appliance to obfuscate the household's power demand. We evaluate and compare both load hiding techniques on real household data and show that both techniques can strengthen household privacy but only LLH can increase appliance level privacy

Integration of Legacy Appliances into Home Energy Management Systems

The progressive installation of renewable energy sources requires the coordination of energy consuming devices. At consumer level, this coordination can be done by a home energy management system (HEMS). Interoperability issues need to be solved among smart appliances as well as between smart and non-smart, i.e., legacy devices. We expect current standardization efforts to soon provide technologies to design smart appliances in order to cope with the current interoperability issues. Nevertheless, common electrical devices affect energy consumption significantly and therefore deserve consideration within energy management applications. This paper discusses the integration of smart and legacy devices into a generic system architecture and, subsequently, elaborates the requirements and components which are necessary to realize such an architecture including an application of load detection for the identification of running loads and their integration into existing HEM systems. We assess the feasibility of such an approach with a case study based on a measurement campaign on real households. We show how the information of detected appliances can be extracted in order to create device profiles allowing for their integration and management within a HEMS

GREEND: An Energy Consumption Dataset of Households in Italy and Austria

Home energy management systems can be used to monitor and optimize consumption and local production from renewable energy. To assess solutions before their deployment, researchers and designers of those systems demand for energy consumption datasets. In this paper, we present the GREEND dataset, containing detailed power usage information obtained through a measurement campaign in households in Austria and Italy. We provide a description of consumption scenarios and discuss design choices for the sensing infrastructure. Finally, we benchmark the dataset with state-of-the-art techniques in load disaggregation, occupancy detection and appliance usage mining

Load disaggregation applications using active power measurements

Das Smart Grid hat das Ziel das heutige Stromnetz zu verbessern um efifzienter und zuverlässiger zu arbeiten. Es soll dem Stromnetz ermöglichen in einem optimalen und sicheren Zustand zu funktionieren, obwohl eine steigende Integration von verteilten erneuerbaren Energieerzeugungseinheiten zu erwarten ist. Ein Schlüsselfaktor, um dieses Ziel zu erreichen, ist die Einführung von Smart Metering, welches feinkörnige Messungen bietet, um zu wissen, wann und wo, wie viel Energie verbraucht wurde. Die Verfügbarkeit der feinkörnigen Verbrauchsinformationen hilft dabei das Energiebewusstsein der Einwohner zu verbessern, was zu einer effizienteren Nutzung von Energieressourcen führt. Im Allgemeinen sind die zu erwartenden Einsparungen um so höher je detaillierter die Informationen über den Energieverbrauch sind. Ein möglicher Ansatz Verbrauchsinformationen auf Geräteebene zu beurteilen, ist es zu jedem Gerat eine Messeinheit hinzuzufügen. Dies führt zu zusätzlichen Kosten und erhöht auch den Energieverbrauch aufgrund der zusätzlichen Messeinheiten. Non-intrusive load monitoring (NILM) versucht in diesem Zusammenhang den Haushaltsverbrauch auf die Gerätekomponenten mittels eines zentralen Messansatzes mit minimalen Kosten der Messeinheiten herunter zu brechen. Die grundsätzliche Idee von NILM ist es statistische Informationen des Geräteverbrauches zu nutzen und dieses Wissen zu einem Klassi fizierungsmechanismus zu führen, um laufende Gerate zu erkennen. Diese Doktorarbeit befasst sich mit drei verschiedenen Anwendungen für Nonintrusive Load Monitoring. Zunächst wird ein einfacher Optimierungsansatz vorgeschlagen, um das Problem von aggregierten Stromverbrauchern zu lösen. Sechs verschiedene metaheuristische Optimierungsverfahren werden dafür verwendet und mit realen Daten getestet. Die Auswertung ergab, dass das Verfahren für einfache Kon figurationen möglich ist, aber nicht mit Gerätekon figurationen, die typisch für einen Haushalt sind, umgehen kann. Um die Komplexität zu beurteilen, befasst sich diese Arbeit mit zwei Komplexitätsmaßen zur Klassi fizierung des NILM-Problems. Diese Anwendung wurde durch die Tatsache inspiriert, dass es keine generell übliche Problemdefinition für NILM gibt. Verschiedenste NILM-Ansatze verwenden reale Datensatze mit verschiedenen Vorbearbeitungsstufen und Systemannahmen. Ein fairer Vergleich zwischen verschiedenen NILM-Algorithmen ist daher ohne ein Komplexitätsmaß zur Beschreibung eines NILM-Problems nicht möglich. Die Evaluierung mittels drei verschiedenen Verbrauchsdatensätzen zeigte, dass die vorgeschlagenen Komplexitätsmaße dazu geeignet sind, das NILM-Problem entsprechend ihrer Komplexität zu klassifi zieren. Schlussendlich, führt diese Arbeit auch noch einen neuen unbeobachteten (unsupervised) NILM-Ansatz ein. Dieser Ansatz funktioniert ohne Systeminformationen und verbessert ständig die Systeminformationen. Er arbeitet online und ist fähig auf Embedded-Hardware zu laufen. Der Ansatz wurde mit künstlichen und realen Szenarien auf seine Anwendbarkeit und Nützlichkeit für NILMAnwendungen hin überprüft.The Smart Grid is aiming at improving today's power grid to work more efficient and more reliable. It should enable the grid to work in an optimal and safe way even with an expected increased integration of a high number of distributed renewable energies generation units. One key factor to achieve these goals is to introduce smart metering providing fine-grained measurements letting us know when and where how much energy was consumed. The availability of fine-grained consumption information helps to improve energy awareness of inhabitants which leads to a more efficient use of energy resources. In general, the more detailed the information of energy consumption the higher are the expected savings. Thus, feedback on the energy consumption of particular devices is beneficial to increase energy savings. One possible approach to assess consumption information on device-level is to add a measurement unit to each appliance. This introduces additional costs and increases also the energy consumption due to the additional metering units. In this context, Non-intrusive Load Monitoring (NILM) tries to break down the household consumption data to its appliance components at the grid connection point with minimum costs. The basic idea of NILM is to use statistical information of the appliance usage and to apply this knowledge for detecting running appliances in the overall power consumption. This thesis deals with three different applications for non-intrusive load monitoring. First, a simple optimization based approach is proposed to solve the problem of aggregated power loads. Six different metaheuristic optimization techniques are used and tested on real-world data. The evaluation showed that the procedure is possible for simple setups, but cannot deal with device configurations that are typical for households. Furthermore, to assess the complexity of NILM, the thesis is dealing with two complexity measures for classifying the load disaggregation problem. This application was inspired by the fact that there is no general common problem definition for NILM. Different NILM evaluations are using real-world datasets with different pre-processing stages and system assumptions. A fair comparison between different NILM problems is only possible with a complexity measure describing the NILM problem. The evaluations on three different real-world datasets showed that the proposed complexity measures are suitable to classify load disaggregation problems according to their complexity. Finally, the thesis introduces a new unsupervised NILM approach. This approach is working without system information and is improving its system knowledge over time. It is working online and is suitable to run on embedded hardware. The applicability and the usefulness for NILM applications has been evaluated with synthetic and real-world data.Dominik EgarterZusammenfassung in deutscher SpracheAlpen Adria Universität Klagenfurt, Dissertation, 2015OeBB(VLID)241379