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Abstract
Energy disaggregation is the process of disaggregating a household's total energy consumption into its appliance-level components. One of the limitations of energy disaggregation is its generalization capacity, which can be defined as the ability of the model to analyze new households. In this article, a new energy disaggregation approach based on adversarial autoencoder (AAE) is proposed to create a generative model and enhance the generalization capacity. The proposed method has a probabilistic structure to handle uncertainties in the unseen data. By transforming the latent space from a deterministic structure to a Gaussian prior distribution, AAEs decoder transforms into a generative model. The proposed approach is validated through experimental tests using two different datasets. The experimental results exhibit a 55% MAE performance increase compared to deterministic models and 7% compared to probabilistic models. In addition, considering the predictions made when the appliances are on, the AAE improves the performance by 16% for UKDALE and 36% for REDD dataset compared to the state-of-art models. Moreover, the online analysis performance of AAE is examined in detail, and the disadvantages of instant predictions and the possible solutions are extensively discussed.
Original language | English |
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Journal | IEEE Transactions on Industrial Informatics |
Volume | 18 |
Issue number | 12 |
Pages (from-to) | 8399-8408 |
Number of pages | 10 |
ISSN | 1551-3203 |
DOIs | |
Publication status | Published - Dec 2022 |
Bibliographical note
Publisher Copyright:IEEE
Keywords
- Adversarial autoencoder
- Deep learning
- Energy disaggregation
- Generative adversarial networks
- Nonintrusive load monitoring (NILM)
- Online energy disaggregation
- Probabilistic energy disaggregation
- Residential energy disaggregation
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Dive into the research topics of 'Deep Learning-based Probabilistic Autoencoder for Residential Energy Disaggregation: An Adversarial Approach'. Together they form a unique fingerprint.Projects
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CROM: Center for Research on Microgrids
Guerrero, J. M., Vasquez, J. C., Tinajero, G. D. A., Akhavan, A. & Guldbæk, B. K.
01/08/2019 → 31/07/2025
Project: Research