Model predictive control of a thermally activated building system to improve energy management of an experimental building: Part I—Modeling and measurements
SEMPEY, Alain
Université de Bordeaux [UB]
La Rochelle Université [ULR]
Institut de Mécanique et d'Ingénierie [I2M]
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Université de Bordeaux [UB]
La Rochelle Université [ULR]
Institut de Mécanique et d'Ingénierie [I2M]
SEMPEY, Alain
Université de Bordeaux [UB]
La Rochelle Université [ULR]
Institut de Mécanique et d'Ingénierie [I2M]
< Reduce
Université de Bordeaux [UB]
La Rochelle Université [ULR]
Institut de Mécanique et d'Ingénierie [I2M]
Language
EN
Article de revue
This item was published in
Energy and Buildings. 2018-05-05 n° 172, p. 94-103
English Abstract
A simple way to reduce energy consumption is to minimize heating use during unoccupied periods. This implies the possibility of adjusting the room temperature setpoint. However, systems with a large thermal capacity cannot ...Read more >
A simple way to reduce energy consumption is to minimize heating use during unoccupied periods. This implies the possibility of adjusting the room temperature setpoint. However, systems with a large thermal capacity cannot follow sudden setpoint changes because of their thermal inertia. A model predictive con- trol (MPC) allows the harnessing of this inertia in order to reduce heating costs and improve comfort. This advanced control technique is based on disturbances anticipation (occupation, weather conditions) and requires a model of the system which has to be controlled. Therefore, the use of such controller needs a reliable model that describes well the dynamics of the room on upcoming days. This paper presents a method for the selection of the model (type, level of complexity) to be implemented in a MPC controller to anticipate the control of a long time response floor heating system on a real building. The demonstra- tion room and embedded systems serving as experimental support are presented. Short measurement periods are carried out to identify the model parameter values minimizing the gap between model out- put and measurement. Gray-box models based on electrical analogy and state-space representation are proposed. They are constructed from physical knowledge and then identified by choosing the most ap- propriate measurement series. A sensitivity analysis method (Morris) is used to improve the quality of the identified model which satisfies control criteria with two specific validation measurement series. In a complementary paper, the predictive controller integrating the selected model is compared to more conventional management strategies in simulation and on-site with the experimental building.Read less <
English Keywords
Low order model
Building parameter identification
Instrumentation
Model predictive control
ANR Project
PREdiction et Contrôle Commande Intelligent par la Simulation et l'Optimisation Numérique - ANR-12-VBDU-0006