2019

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• Benjamin Völker, Phillip M Scholl, Bernd Becker
  Semi-Automatic Generation and Labeling of Training Data for Non-intrusive Load Monitoring
  2019 e-Energy '19 Proceedings of the Tenth ACM International Conference on Future Energy Systems ACM New York, NY, USA ©2019, Band: 10, Seiten: 17 - 23
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  Kurzfassung

  User awareness is one of the main drivers to reduce unnecessary energy consumption in our homes. This awareness, however, requires individual energy data of the devices we own. A retrofittable way to get this data is to use Non-Intrusive Load Monitoring methods. Most of these methods are supervised and require to collect labeled ground truth data in advance. Labeling on-phases of devices is already a tedious process, but if further information about internal device states are required (e.g. intensity of an HVAC), manual labeling methods are infeasible. We propose a novel data collection and labeling method for Non-Intrusive Load Monitoring. This method uses intrusive sensors directly connected to the monitored devices. A post-processing step classifies the connected devices into four categories and exposes internal state sequences in a semi-automatic way. We evaluated our labeling method with a sample dataset comparing the amount of recognized events, states and classified device category. The event detector achieved a total F1 score of 86.52 % for devices which show distinct states in its power signal. Using our framework, the overall labeling effort is cut by more than half (42%).

2018

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• Benjamin Völker, Philipp Scholl, Bernd Becker
  Towards the Fusion of Intrusive and Non-intrusive Load Monitoring - A Hybrid Approach
  2018 e-Energy '18 Proceedings of the Ninth International Conference on Future Energy Systems/Karlsruhe, Germany , Seiten: 436 - 438
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  Kurzfassung

  With Electricity as a fundamental part of our life, its production has still large, negative environmental impact. Therefore, one strain of research is to optimize electricity usage by avoiding its unnecessary consumption or time its consumption when green energy is available. The shift towards an Advanced Metering Infrastructure (AMI) allows to optimize energy distribution based on the current load at residence level. However, applications such as Demand Management and Advanced Load Forecasting require information further down at device level, which cannot be provided by standard electricity meters nor existing AMIs. Hence, different approaches for appliance monitoring emerged over the past 30 years which are categorized into Intrusive systems requiring multiple distributed sensors and Non-Intrusive systems requiring a single unobtrusive sensor. Although each category has been individually explored, hybrid approaches have received little attention. Our experiments highlight that variable consumer devices (e.g. PCs) are detrimental to the detection performance of non-intrusive systems. We further show that their influence can be inhibited by using sensor data from additional intrusive sensors. Even fairly straightforward sensor fusion techniques lead to a classification performance (F1) gain from 84.88 % to 93.41 % in our test setup. As this highlights the potential to contribute to the global goal of saving energy, we define further research directions for hybrid load monitoring systems

2017

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• Tobias Schubert, Benjamin Völker, Marc Pfeifer, Bernd Becker
  The Smart MiniFab: An Industrial IoT Demonstrator Anywhere at Any Time
  2017 Smart Education and e-Learning 2017, Vilamoura/Portugal, KES International Springer International Publishing, Seiten: 253 - 262

2016

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• Benjamin Völker, Tobias Schubert, Bernd Becker
  iHouse: A Voice-Controlled, Centralized, Retrospective Smart Home
  2016 7th EAI International Conference on Sensor Systems and Software
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  Kurzfassung

  Speech recognition in smart home systems has become pop- ular in both, research and consumer areas. This paper introduces an in- novative concept for a modular, customizable, and voice-controlled smart home system. The system combines the advantages of distributed and centralized processing to enable a secure as well as highly modular plat- form and allows to add existing non-smart components retrospectively into the smart environment. To interact with the system in the most com- fortable way - and in particular without additional devices like smart- phones - voice-controlling was added as the means of choice. The task of speech recognition is partitioned into decentral Wake-Up-Word (WUW) recognition and central continuous speech recognition to enable flexibil- ity while maintaining security. This is achieved utilizing a novel WUW algorithm suitable to be executed on small microcontrollers which uses Mel Frequency Cepstral Coefficients as well as Dynamic Time Warping. A high rejection rate up to 99.93% was achieved, justifying the use of the algorithm as a voice trigger in the developed smart home system.