|Datum||Fr 22. April 2022, 11:30 Uhr|
|Ort||Raum 348 (Gebäude 50.34)|
|Vorheriger Termin||Fr 1. April 2022|
|Nächster Termin||Fr 29. April 2022|
|Titel||On the Utility of Privacy Measures for Battery-Based Load Hiding|
|Kurzfassung||Hybrid presentation : https://kit-lecture.zoom.us/j/67744231815
Battery based load hiding gained a lot of popularity in recent years as an attempt to guarantee a certain degree of privacy for users in smart grids. Our work evaluates a set of the most common privacy measures for BBLH. For this purpose we define logical natural requirements and score how well each privacy measure complies to each requirement. We achieve this by scoring the response for load profile altering (e.g. noise addition) using measures of displacement. We also investigate the stability of privacy measures toward load profile length and number of bins using specific synthetic data experiments. Results show that certain private measures fail badly to one or many requirements and therefore should be avoided.
|Titel||Theory-guided Load Disaggregation in an Industrial Environment|
|Kurzfassung||The goal of Load Disaggregation (or Non-intrusive Load Monitoring) is to infer the energy consumption of individual appliances from their aggregated consumption. This facilitates energy savings and efficient energy management, especially in the industrial sector.
However, previous research showed that Load Disaggregation underperforms in the industrial setting compared to the household setting. Also, the domain knowledge available about industrial processes remains unused.
The objective of this thesis was to improve load disaggregation algorithms by incorporating domain knowledge in an industrial setting. First, we identified and formalized several domain knowledge types that exist in the industry. Then, we proposed various ways to incorporate them into the Load Disaggregation algorithms, including Theory-Guided Ensembling, Theory-Guided Postprocessing, and Theory-Guided Architecture. Finally, we implemented and evaluated the proposed methods.
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