Disaggregation of Community Level Energy Data to Individual Households

Using sequence-to-point learning

Bachelor thesis (2023)

Authors

S. Verlaan Electrical Engineering, Mathematics and Computer Science

Contributors

S.K. Kuilman Interactive Intelligence - EEMCS (mentor)
L. Cavalcante Siebert Interactive Intelligence - EEMCS (mentor)
M.M. de Weerdt Algorithmics - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Stan Verlaan
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Published Date

28-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Non-intrusive load monitoring (NILM) is a well-researched concept that aims to provide insights into individual appliance energy usage without the need for dedicated meters. This paper explores the possibility of applying the NILM concept to disaggregate energy data from a community level to a household level. By doing so, it addresses privacy concerns associated with real-time household data collection through smart meters. The proposed approach leverages sequence-to-point learning, a deep learning method, to perform the disaggregation. A method that showed promising results in the domain of NILM. The results show good performance on the evaluation metrics, but they also show that the model does a poor job of capturing the load signature.

Files

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