Adaptive model predictive climate control of multi-unit buildings using weather forecast data

Abstract Energy use in buildings contributes a large part in global energy demand. To reduce energy use in this group of consumers, specially in cold seasons, an automatic control technique is proposed. In this paper, a model predictive controller (MPC) is employed to minimize the boiler activation time. The method uses the building model and incorporates the weather forecast data to act on the actuator in an optimal fashion while treating the user comfort constraints. This technique, as a part, can be embedded into the building energy management system. The building model parameters are obtained via an online identification process using unscented kalman filter (UKF). This identification is performed on-the-fly so the model of a building is continuously updated. The results of the system identification as well as the control performance are shown via Monte Carlo simulations, and compared with the results of a conventional control law. The comparison shows that the proposed method saves % 13 energy consumption in the boiler activation.

[1]  James E. Braun,et al.  System identification for building thermal systems under the presence of unmeasured disturbances in closed loop operation: Lumped disturbance modeling approach , 2016 .

[2]  Emrah Biyik,et al.  A predictive control strategy for optimal management of peak load, thermal comfort, energy storage and renewables in multi-zone buildings , 2019, Journal of Building Engineering.

[3]  Alberto Sangiovanni-Vincentelli,et al.  Building Efficiency and Sustainability in the Tropics ( SinBerBEST ) Title Online Simultaneous State Estimation and Parameter Adaptation for Building Predictive , 2013 .

[4]  Man Pun Wan,et al.  Experimental study of a model predictive control system for active chilled beam (ACB) air-conditioning system , 2019, Energy and Buildings.

[5]  Brandon Hencey,et al.  Online building thermal parameter estimation via Unscented Kalman Filtering , 2012, 2012 American Control Conference (ACC).

[6]  Jin Wen,et al.  Development and Validation of Online Parameter Estimation for HVAC Systems , 2003 .

[7]  Jin Wen,et al.  Development and validation of online models with parameter estimation for a building zone with VAV system , 2007 .

[8]  Tian Zhang,et al.  Model Predictive Control for Building Energy Reduction and Temperature Regulation , 2018, 2018 IEEE Green Technologies Conference (GreenTech).

[9]  Julio Ariel Romero,et al.  A simplified black-box model oriented to chilled water temperature control in a variable speed vapour compression system , 2011 .

[10]  Zheng Qin,et al.  Experimental implementation of whole building MPC with zone based thermal comfort adjustments , 2017 .

[11]  Mohamed Benbouzid,et al.  Model Predictive Control-based Thermal Comfort and Energy Optimization , 2019, IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society.

[12]  Barbara Mayer,et al.  Effective fuzzy black-box modeling for building heating dynamics , 2015 .

[13]  Manfred Morari,et al.  BRCM Matlab Toolbox: Model generation for model predictive building control , 2014, 2014 American Control Conference.

[14]  James E. Braun,et al.  System identification for building thermal systems under the presence of unmeasured disturbances in closed loop operation: Theoretical analysis and application , 2017 .

[15]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[16]  Zhe Zhang,et al.  A state-space thermal model incorporating humidity and thermal comfort for model predictive control in buildings , 2018, Energy and Buildings.

[17]  Gerardo Maria Mauro,et al.  Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort , 2016 .

[18]  D. Simon.  Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[19]  Bernard Ghanem,et al.  An Exact Penalty Method for Binary Optimization Based on MPEC Formulation , 2017, AAAI.

[20]  Damien Picard,et al.  Impact of the controller model complexity on model predictive control performance for buildings , 2017 .

[21]  Gabriele Comodi,et al.  Data-driven models for short-term thermal behaviour prediction in real buildings , 2017 .

[22]  Christian Ghiaus,et al.  Optimal temperature control of intermittently heated buildings using Model Predictive Control: Part I – Building modeling , 2012 .

[23]  Jin Wen,et al.  Review of building energy modeling for control and operation , 2014 .

[24]  Manfred Morari,et al.  Model Predictive Climate Control of a Swiss Office Building: Implementation, Results, and Cost–Benefit Analysis , 2016, IEEE Transactions on Control Systems Technology.

[25]  Frauke Oldewurtel,et al.  Building modeling as a crucial part for building predictive control , 2013 .

[26]  Martin Kozek,et al.  Ten questions concerning model predictive control for energy efficient buildings , 2016 .

[27]  William O'Brien,et al.  Sequential state prediction and parameter estimation with constrained dual extended Kalman filter for building zone thermal responses , 2019, Energy and Buildings.

[28]  Francesco Borrelli,et al.  A distributed predictive control approach to building temperature regulation , 2011, Proceedings of the 2011 American Control Conference.

[29]  Murat Hosoz,et al.  Artificial neural network analysis of a refrigeration system with an evaporative condenser , 2006 .

[30]  Fan Zhang,et al.  Passive versus active learning in operation and adaptive maintenance of Heating, Ventilation, and Air Conditioning , 2019, Applied Energy.