Abstract
We propose to study the dynamic behavior of indoor temperature and energy consumption in a cold room during demand response periods. Demand response is a method that consists in smoothing demand over time, seeking to reduce or even stop consumption during periods of high demand in order to shift it to periods of lower demand. Such a system can therefore be tackled as the study of a time-series, where each behavioral parameter is a time-varying parameter. Four deep neural network architectures derived from the LSTM architecture were studied, adapted and compared. Their validation was carried out using experimental data collected in a cold room in order to assess their performance in predicting demand response.
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Mellouli, N., Akerma, M., Hoang, M., Leducq, D., Delahaye, A. (2019). Deep Learning Models for Time Series Forecasting of Indoor Temperature and Energy Consumption in a Cold Room. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11684. Springer, Cham. https://doi.org/10.1007/978-3-030-28374-2_12
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