Wilfried Elmenreich: Electric Load Disaggregation as a Means for Reducing Energy Consumption

  Рет қаралды 28

TU Wien Informatics

TU Wien Informatics

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Public Lecture Series "Sustainability in Computer Science".
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Speaker:
Wilfried Elmenreich, Full Professor and heads the Research Unit Smart Grids at University of Klagenfurt
Abstract:
When the EU introduced smart meters, the idea was to give consumers more information about their power consumption, thus raising energy awareness. Tailored appliance-level feedback is expected to lead to more careful use of energy and thus to savings of around 12%. Despite the potential benefits, implementing a network of small measurement devices, each per appliance/socket has raised concerns due to the added energy consumption and intrusive visibility of smart adapters impacting user experience. Non-Intrusive Load Monitoring employs computer algorithms to extract appliance consumption data from a single, smart meter measuring the entire system’s consumption. While this concept has been around since 1985, when George W. Hart (MIT) proposed it in a report for the Electric Power Research Institute, recent advancements in computer science have propelled its potential. This talk explores the significance of load disaggregation and its relevance in today’s context, where smart meters and small embedded devices like the Raspberry Pi offer sufficient computational resources. The presentation delves into the evolution of load disaggregation algorithms, from traditional bin-packing techniques and Hidden Markov Models to the cutting-edge application of deep neural networks powered by machine learning. As these techniques continue to evolve, the talk also addresses the pressing need to address privacy issues and develop comprehensive, long-term interfaces that engage consumers in a captivating manner to monitor their power consumption effectively. In conclusion, the talk highlights the potential of Electric Load Disaggregation in increasing energy awareness and reducing energy consumption. Leveraging advancements in computer science, this technology promises to substantially impact sustainable energy practices, empowering consumers to conserve energy and promote a greener future.

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