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|Title:||Deep Learning-Based Probabilistic Autoencoder for Residential Energy Disaggregation: An Adversarial Approach||Authors:||Çimen, Halil
Vasquez, Juan C.
Guerrero, Josep M.
|Keywords:||Hidden Markov models
Generative adversarial networks
Adversarial autoencoder (AAE)
generative adversarial networks
nonintrusive load monitoring (NILM)
online energy disaggregation
probabilistic energy disaggregation
residential energy disaggregation
|Issue Date:||2022||Publisher:||Ieee-Inst Electrical Electronics Engineers Inc||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.||URI:||https://doi.org/10.1109/TII.2022.3150334
|Appears in Collections:||Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu|
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections
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