Improving the Generalisability of Deep Learning NILM Algorithms using One-Shot Transfer Learning

Can one-shot transfer learning be leveraged to enhance the performance of a CNN-based NILM algorithm on unseen data?

Bachelor thesis (2023)

Authors

W.J.M. Verschuren Electrical Engineering, Mathematics and Computer Science

Contributors

L. Cavalcante Siebert Interactive Intelligence - EEMCS (supervisor 1)
S.K. Kuilman Interactive Intelligence - EEMCS (supervisor 1)
M.M. de Weerdt Algorithmics - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Wim Verschuren
Convolutional neural network Transfer learning NILM
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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 technique used to disaggregate household power consumption data into individual appliance components without the need for dedicated meters for each appliance. This paper focuses on improving the generalizability of NILM algorithms to unseen households using Convolutional Neural Networks (CNNs) and one-shot transfer learning. The research investigates the effectiveness of one-shot transfer learning in fine-tuning a CNN model to accurately detect the ON/OFF state of appliances in households not seen during the training phase of the CNN. The study utilizes the Pecan Street dataset for training and evaluation, which includes detailed energy consumption records from various locations in the United States. The results suggest that one-shot transfer learning could enhance the performance of the NILM algorithm, particularly when multiple data samples are used for fine-tuning. However, the effectiveness of one-shot transfer learning varies strongly depending on the number of samples and the characteristics of the target household.