A numerical and experimental study of a simple model-based predictive control strategy in a perimeter zone with phase change material. (21st October 2018)

• Record Type: Journal Article
• Authors: Papachristou, Anastasios C.; Vallianos, Charalampos A.; Dermardiros, Vasken; Athienitis, Andreas K.; Candanedo, JosÉ A.
• Is Part Of: Science and technology for the built environment. Volume 24:Number 9(2018)
• Page Start: 933
• Page End: 944
• Publication Date: 2018-10-21
• Subjects: Heating -- Periodicals; Ventilation -- Periodicals; Air conditioning -- Periodicals; Refrigeration and refrigerating machinery -- Periodicals; Indoor air quality -- Periodicals; Indoor air quality; Air conditioning; Heating; Refrigeration and refrigerating machinery; Ventilation; Periodicals; 697
• DOI: 10.1080/23744731.2018.1438011
• Languages: English
• ISSNs: 2374-474X

Abstract

Abstract : The current article presents a numerical and experimental study of predictive control strategies based on a low-order model in a test cell that emulates a perimeter zone of a building. The test cell uses a phase change material as a means of thermal storage. The phase change material, embedded in the wall of the test cell furthest away from the window, is thermally actively charged through forced air circulation. The objective of the study is to investigate how model-based predictive control can be used to optimize the performance of a phase change material wall. The present article also shows how a low-order thermal network model can be used as an effective tool in the design and implementation of the model-based predictive control strategy. The proposed model predictive control algorithm uses a set of linear ramp functions to change the room temperature set-point to reduce and shift peak power demand. These ramp set-point profiles allow the effective charging and discharging of the wall-integrated phase change material. The algorithm applied in the experimental facility uses the outdoor temperature as an input to select the best charging and discharging rates over a prediction horizon. A low-order model of the room and the phase change material wall is used in the predictive control algorithm. It was found that this model can accurately predict the peak power demand (coefficient of variation of the root-mean-square error 28.2% and normalized mean bias error 3.4%) and the room temperature profile. As the process moves forward in time, the weather profile is updated periodically and the algorithm calculates the new outputs over the new control horizon. The whole procedure is automated and the outputs of the algorithm are transferred to the test room controller through BACnet.