Abstract
The implementation of model predictive controls (MPCs) in buildings represents an important opportunity to reduce energy consumption and to apply demand side management strategies. In order to be effective, the MPC should be provided with an accurate model that is able to forecast the actual building energy demand. To this aim, in this paper, a data-driven model realized with an artificial neural network is compared to a physical-based resistance–capacitance (RC) network in an operative MPC. The MPC was designed to minimize the total cost for the thermal demand requirements by unlocking the energy flexibility in the building envelope, on the basis of price signals. Although both models allow energy cost savings (about 16% compared to a standard set-point control), a deterioration in the prediction performance is observed when the models actually operate in the controller (the root mean square error, RMSE, for the air zone prediction is about 1 °C). However, a difference in the on-time control actions is noted when the two models are compared. With a maximum deviation of 0.5 °C from the indoor set-point temperature, the physical-based model shows better performance in following the system dynamics, while the value rises to 1.8 °C in presence of the data-driven model for the analyzed case study. This result is mainly related to difficulties in properly training data-driven models for applications involving energy flexibility exploitation.
Highlights
Advanced control methods for energy management in buildings are required if the goal is obtaining an optimized operational performance [1]
model predictive controls (MPCs) can be applied for many purposes: (i) to exploit the energy storage capability in high-massive buildings, (ii) to maximize the use of renewable energy sources (RES), or (iii) to implement demand side management (DSM) such as demand response (DR)
2, the evaluation the forecast performance ofbuilding the twoprediction building models wasmodels realized according two points view: (i) the ability(i)ofthe theability modelsoftothe match the to behavior prediction was realizedtoaccording toof two points of view: models match of a known reference building and (ii) their dynamic operation when applied within the controller of the behavior of a known reference building and (ii) their dynamic operation when applied within the the same building
Summary
Advanced control methods for energy management in buildings are required if the goal is obtaining an optimized operational performance [1]. The basic concept of MPC is to use a dynamic model to forecast a system behavior and to optimize the actuations in order to operate under the best sequence of decisions [5]. A key feature of MPCs consists in selecting future control actions, taking into account both predictions of future disturbances and system constraints [4], while the goal is pursued. MPCs can be applied for many purposes: (i) to exploit the energy storage capability in high-massive buildings, (ii) to maximize the use of renewable energy sources (RES), or (iii) to implement demand side management (DSM) such as demand response (DR). In order to be truly effective, an MPC must be based on a reliable model of the system under study [6]
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