Print Email Facebook Twitter Improving the Generalisability of Deep Learning NILM Algorithms using One-Shot Transfer Learning Title 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? Author Verschuren, Wim (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Cavalcante Siebert, L. (mentor) Kuilman, S.K. (mentor) de Weerdt, M.M. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2023-06-28 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. Subject NILMtransfer learningconvolutional neural network To reference this document use: http://resolver.tudelft.nl/uuid:12b6ff8d-28ed-4741-9b30-2a2da33ca779 Part of collection Student theses Document type bachelor thesis Rights © 2023 Wim Verschuren Files PDF Wim_Verschuren_FINAL_3_.pdf 178.82 KB Close viewer /islandora/object/uuid:12b6ff8d-28ed-4741-9b30-2a2da33ca779/datastream/OBJ/view