Modeling and Control Electric and Heating Sources in a Building Using MPC
الموضوعات :Rasoul Moradimehr 1 , Esmaeil Alibeiki 2 , Seyyed Mostafa Ghadami 3
1 - Department of Electrical Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran
2 - Department of Electrical Engineering,
Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran
3 - Department of Electrical Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul ,Iran
الکلمات المفتاحية: energy consumption, Building energy management, MPC method,
ملخص المقالة :
Saving energy in various sections of the building and improving productivity in the face of energy crisis and environmental pollution can be one of the main challenges in the world and our country. In this paper, based on the necessity of controlling and minimizing energy consumption by considering the maximum optimal temperature conditions in different areas of the building, a control method based on the predictive controller is used, which takes into account the effects of the time of use for sources. The predictive controller predicts the optimal control input for future moments according to the definition of the cost function based on the ambient temperature error and the weighted expression of the control input. In this paper, the issue of optimization is thoroughly studied, evaluated, and simulated. The temperature change model in buildings and the proposed control scheme are implemented in MATLAB software and the system is simulated using the building environment. The simulation is performed under several different scenarios of time of use and conditions to show the performance of the proposed design. Based on the results of the proposed control method, the accuracy and performance of the model in different scenarios of building conditions are acceptable.
[1] B. Dong, V. Prakash, F. Feng, and Z. O’Neill, “A review of a smart building sensing system for better indoor environment control,” Energy and Buildings, vol. 199. pp. 29–46, 2019.
[2] G. Lymperopoulos and P. Ioannou, “Adaptive control of Networked Distributed Systems with unknown interconnections,” in 2016 IEEE 55th Conference on Decision and Control,pp. 3456–3461, 2016.
[3] G. Lymperopoulos and P. Ioannou, “Model Reference Adaptive Control for Networked Distributed Systems with Strong Interconnections and Communication Delays,” J. Syst. Sci. Complex., vol. 31, no. 1, pp. 38–68, 2018.
[4] S. A. Barakat, “Inter-zone convective heat transfer in buildings: A review,” J. Sol. Energy Eng. Trans. ASME, vol. 109, no. 2, pp. 77–78, 1987.
[5] S. Mărgulescuet al., “Characteristics of buoyant flow from open windows in naturally ventilated rooms,” Energy Build., vol. 40, no. 2, 2014.
[6] G. Lymperopoulos and P. Ioannou, “Distributed Adaptive Control of Multi-Zone HVAC Systems,” in 27th Mediterranean Conference on Control and Automation, MED 2019 - Proceedings, pp. 553–558, 2016.
[7] G. Lymperopoulos, P. M. Papadopoulos, P. Ioannou, and M. M. Polycarpou, “Distributed Adaptive Control of Air Handling Units for Interconnected Building Zones,” in Proceedings of the American Control Conference, pp. 4207–4212, 2020.
[8] M. Elnour, N. Meskin, Multi-zone HVAC control system design using
feedback linearization, in 2017 5th International Conference on Control, Instrumentation, and Automation (ICCIA), IEEE, 2017.
[9] J. Yang, M. Santamouris, and S. E. Lee, “Review of occupancy sensing systems and occupancy modeling methodologies for the application in institutional buildings,” Energy Build., vol. 121, pp. 344–349, 2016.
[10] Z. Chen, Y. Wang, and H. Liu, “Unobtrusive sensor-based occupancy facing direction detection and tracking using advanced machine learning algorithms,” IEEE Sens. J., vol. 18, no. 15, pp. 6360–6368, 2018.
[11] S. D’Oca, T. Hong, and J. Langevin, “The human dimensions of energy use in buildings: A review,” Renewable and Sustainable Energy Reviews, vol. 81. pp. 731–742, 2018.
[12] X. Chen, Q. Wang, and J. Srebric, “Occupant feedback based model predictive control for thermal comfort and energy optimization: A chamber experimental evaluation,” Appl. Energy, vol. 164, pp. 341–351, 2016.
[13] D. Gonçalves, Y. Sheikhnejad, M. Oliveira, and N. Martins, “One step forward toward smart city Utopia: Smart building energy management based on adaptive surrogate modeling,” Energy Build., vol. 223, 2020.
[14] G. Lymperopoulos and P. Ioannou, “Building temperature regulation in a multi-zone HVAC system using distributed adaptive control,” Energy Build., vol. 215, 2020.
[15] Z. Wang, G. Hu, and C. J. Spanos, “Distributed model predictive control of bilinear HVAC systems using a convexification method,” in 2017 Asian Control Conference, vol. 201, pp. 1608–1613, 2018.
[16] E. Camponogara, H. Scherer, L. Biegler, and I. Grossmann, “Hierarchical decompositions for MPC of resource constrained control systems: applications to building energy management,” Optim. Eng., vol. 22, no. 1, pp. 187–215, 2021.
