Abstract
Energy management increasingly requires tools to support decisions for improving consumption. This is achieved not only obtaining feedback from current systems but also using prior knowledge about human behaviour. The advances of data-driven models provide techniques like Non-Intrusive Load Monitoring (NILM) which are capable of estimating energy demand of appliances from total consumption. In addition, deep learning models have improved accuracy in energy disaggregation using separated networks for each device. However, the complexity can increase in large facilities and feedback may be impaired for a proper interpretation. In this work, a deep neural network based on a Fully Convolutional denoising AutoEncoder is proposed for energy disaggregation that uses a conditioning input to modulate the estimation aimed to one specific appliance. The model performs a complete disaggregation using a network whose modulation to target the estimation can be steered by the user. Experiments are done using data from a hospital facility and evaluating reconstruction errors and computational efficiency. The results show acceptable errors compared to methods that require various networks and a reduction of the complexity and computational costs, which can allow the user to be integrated into the analysis loop.
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Acknowledgement
This work was supported by the Ministerio de Ciencia e Innovación/Agencia Estatal de Investigación (MCIN/AEI/ 10.13039/501100011033) under grants PID2020-115401GB-I00 and PID2020-117890RB-I00. Data were provided by Hospital of León and SUPPRESS Research Group of University of León within the project DPI2015-69891-C2-1/2-R.
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García, D. et al. (2023). Conditioned Fully Convolutional Denoising Autoencoder for Energy Disaggregation. In: Maglogiannis, I., Iliadis, L., Papaleonidas, A., Chochliouros, I. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023 IFIP WG 12.5 International Workshops. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 677. Springer, Cham. https://doi.org/10.1007/978-3-031-34171-7_34
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