Research on smart grid technologies is expected to result in effective climate change mitigation. Non-Intrusive Load Monitoring (NILM) is seen as a key technique for enabling innovative smart-grid services. By breaking down the energy consumption of households and industrial facilities into its components, NILM techniques provide information on present appliances and can be applied to perform diagnostics. As with related Machine Learning problems, research and development requires a sufficient amount of data to train and validate new approaches. As a viable alternative to collecting datasets in buildings during expensive and time-consuming measurement campaigns, the idea of generating synthetic datasets for NILM gain momentum recently. With SynD, we present a synthetic energy dataset with focus on residential buildings. We release 180 days of synthetic power data on aggregate level (i.e. mains) and individual appliances. SynD is the result of a custom simulation process that relies on power traces of real household appliances. In addition, we present several case studies that demonstrate similarity of our dataset and four real-world energy datasets. Background & Summary Load monitoring is vital for effective and accurate energy monitoring in buildings. Detailed insights can empower further research, help streamlining processes, and improve a building's energy efficiency 1. Introduced in 2 , Non-Intrusive Load Monitoring (NILM) techniques serve to break down a building's aggregate energy consumption to identify active appliances and also to provide diagnostic information. Extensive reviews can be obtained from 3 and 4. NILM can be considered as Machine Learning problem. As such, it requires datasets to train models, to conduct performance evaluation, to evaluate the benefit in real scenarios, and also to perform benchmarking on a common basis. In case of NILM, ground-truth data on aggregate and appliance-level energy consumption are crucial 4. Traditionally, NILM scholarship relies on energy consumption datasets. Such datasets usually contain information on energy consumption on aggregate level (monitored at the mains) and individual loads, which is provided by plug-level meters. Energy consumption datasets are the outcome of measurement campaigns in buildings or industrial facilities, which require expensive measurement equipment, bring bureaucratic burdens, and are time-consuming activities 5. As a viable alternative, the idea of generating synthetic data gain momentum recently. The main motivation behind generating synthetic datasets is to reduce costs for measurement campaigns and save valuable work hours. Instead, custom simulators provide energy consumption datasets on-demand and in contrast to real datasets, without limitations on measurement periods. Furthermore, real datasets suffer from missing readings (gaps), misaligned timestamps, and corrupted data as a result of sensor miscalculation or malfunction 6,7. Synthetic data does not show such issues. With SynD, we present a synthetic energy consumption da...
The integration of renewable energy sources increases the complexity in mantaining the power grid. In particular, the highly dynamic nature of generation and consumption demands for a better utilization of energy resources, which seen the cost of storage infrastructure, can only be achieved through demand-response. Accordingly, the availability of energy and potential overload situations can be reflected using a price signal. The effectiveness of this mechanism arises from the flexibility of device operation, which is nevertheless heavily reliant on the exchange of information between the grid and its consumers. In this paper, we investigate the capability of an interactive energy management system to timely inform users on energy usage, in order to promote an optimal use of local resources. In particular, we analyze data being collected in several households in Italy and Austria to gain insights into usage behavior and drive the design of more effective systems. The outcome is the formulation of energy efficiency policies for residential buildings, as well as the design of an energy management system, consisting of hardware measurement units and a management software. The Mjölnir framework, which we release for open use, provides a platform where various feedback concepts can be implemented and assessed. This * A. Monacchi, M. Herold, D. Egarter and W. Elmenreich are with the Institute of Networked and Embedded Systems, Alpen-Adria-Universität Klagenfurt, F. Versolatto and A. M. Tonello are with WiTiKee Srl 1 arXiv:1505.01311v1 [cs.HC] 6 May 2015includes widgets displaying disaggregated and aggregated consumption information, as well as daily production and tailored advices. The formulated policies were implemented as an advisor widget able to autonomously analyze usage and provide tailored energy feedback.
Sequential Diagnosis methods aim at suggesting a minimal-cost sequence of measurements to identify the root cause of a system failure among the possible fault explanations, called diagnoses. Hitting set algorithms are often used by such methods to precompute a set of diagnoses serving as a decision basis for iterative measurement selection.We show that there are two natural interpretations of the sequential diagnosis problem and argue that (1) existing methods consider only the more general definition of the problem and that (2) tackling the more specific problem might suffice under assumptions commonly met in practice. Thus, we present StaticHS, a novel variant of Reiter’s hitting set tree usable for solving both formulations of the sequential diagnosis problem. Like Reiter’s algorithm, StaticHS is logics- and reasoner-independent and thus generally applicable to various (diagnosis) domains. Theoretical and empirical analyses show the significant superiority of StaticHS to an application of Reiter’s tree in terms of measurement costs when solving both types of sequential diagnosis problems.
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