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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 of 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. Different network topologies are considered, as well as existing approaches for solving multi-step ahead prediction problems. The predictive performance of short-term predictors is also examined with regard to prediction horizon. The performance of the predictors are evaluated using measured data from real scale buildings, showing promising results for the development of accurate prediction tools.

Multivariate Time Series Forecasting with Deep Learning Proceedings in Energy Consumption