Abstract
In summer, a ceiling fan has a tremendous potential to cool rooms, resulting in energy saving and a peak load reduction. During the critical peak pricing (CPP) period of the electricity, one method to attain substantial energy cost saving is to turn off the heating, ventilating and air conditioning (HVAC) system, but this may result in overheating. Fortunately, precooling and ceiling fans can be used to reduce the feeling of hot sensation. Therefore, the best control strategy for an HVAC system and ceiling fans is to maximize energy cost saving without much thermal comfort sacrifice. This paper presents a multi-objective optimization model predictive control (MO-MPC) of precooling temperature setpoint and ceiling fan speed. The proposed MO-MPC integrates an optimization engine, Dakota, and a detailed energy simulation tool, EnergyPlus, to determine the optimal control strategies considering the energy cost and thermal comfort simultaneously. A sub-program of substituting EnergyPlus with decision tree regression models for building climate prediction is deployed. If the calculation time is too long to achieve real-time control, this sub-program can be adopted to accelerate the calculation. In this study, the proposed MO-MPC framework is applied to a small and large building in four different cities with different CPPs. For a summer day, compared with the baseline cases of no precooling and no ceiling fan, the optimal strategies for the small building achieve energy cost savings between 10.3% and 33.3% and a peak power load decrease from 44.0% to 51.9%. Without precooling but with a ceiling fan control, on a particular summer day, for the large building case in Shanghai, 7.4% energy saving is achieved. The optimal control of the precooling temperature and ceiling fans result in a 14.4% energy cost saving. By using the decision tree regression models instead of EnergyPlus, the calculation time of the big office reduces from about 10 hours 40 minutes to 24.4 minutes.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adams BM, Bohnhoff WJ, Dalbey KR, Eddy JP, Eldred S, et al. (2018). DAKOTA, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. Version 6.8 user’s manual. Sandia National Laboratories, Tech. Rep. SAND2014-4633.
Afram A, Janabi-Sharifi F (2014). Theory and applications of HVAC control systems—A review of model predictive control (MPC). Building and Environment, 72: 343–355.
Afram A, Janabi-Sharifi F, Fung AS, Raahemifar K (2017). Artificial neural network (ANN) based model predictive control (MPC) and optimization of HVAC systems: A state of the art review and case study of a residential HVAC system. Energy and Buildings, 141: 96–113.
Al-Sanea SA, Zedan MF, Al-Hussain SN (2012). Effect of thermal mass on performance of insulated building walls and the concept of energy savings potential. Applied Energy, 89: 430–442.
Arabzadeh V, Alimohammadisagvand B, Jokisalo J, Siren K (2018). A novel cost-optimizing demand response control for a heat pump heated residential building. Building Simulation, 11: 533–547.
Arteconi A, Costola D, Hoes P, Hensen JLM (2014). Analysis of control strategies for thermally activated building systems under demand side management mechanisms. Energy and Buildings, 80: 384–393.
Ascione F, Bianco N, de Stasio C, Mauro GM, Vanoli GP (2016). Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort. Energy and Buildings, 111: 131–144.
ASHRAE (2010). Standard 55-2010: Thermal Environmental Conditions for Human Occupancy. Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers.
Building Energy Research Center (2015). 2014 Annual Report on China Building Energy Efficient. Beijing: China Architecture & Building Press. (in Chinese)
Corbin CD, Henze GP, May-Ostendorp P (2013). A model predictive control optimization environment for real-time commercial building application. Journal of Building Performance Simulation, 6: 159–174.
DOE (2017). EnergyPlus 8.7 Version documentation, Input Output Reference. Available at https://www.energyplus.net/documentation. Accessed 15 Jan 2018.
Ghiaus C, Jabbour N (2012). Optimization of multifunction multisource solar systems by design of experiments. Solar Energy, 86: 593–607.
Henze GP, Felsmann C, Knabe G (2004). Evaluation of optimal control for active and passive building thermal storage. International Journal of Thermal Sciences, 43: 173–183.
IEA (2012). World Energy Outlook 2012. International Standards to Develop and Promote Energy Efficient and Renewable Energy Sources, Special ISO Focus—World Energy Congress.
Kwak Y, Huh J-H, Jang C (2015). Development of a model predictive control framework through real-time building energy management system data. Applied Energy, 155: 1–13.
Kwak Y, Huh J-H (2016). Development of a method of real-time building energy simulation for efficient predictive control. Energy Conversion and Management, 113: 220–229.
LBNL (2016). Building Controls Virtual Test Bed. Available at https://simulationresearch.lbl.gov/bcvtb. Accessed at 20 Feb. 2018.
Li S, Zhang D, Roget AB, O’Neill Z (2014). Integrating home energy simulation and dynamic electricity price for demand response study. IEEE Transactions on Smart Grid, 5: 779–788.
Li X, Malkawi A (2016). Multi-objective optimization for thermal mass model predictive control in small and medium size commercial buildings under summer weather conditions. Energy, 112: 1194–1206.
Ma J, Qin J, Salsbury T, Xu P (2012). Demand reduction in building energy systems based on economic model predictive control. Chemical Engineering Science, 67: 92–100.
Nghiem TX (2012). MLE+ Toobox. Available at https://github.com/mlab-upenn/mlep_v1.1. Accessed 21 Feb 2018.
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12: 2825–2830.
Psomas T, Fiorentini M, Kokogiannakis G, Heiselberg P (2017). Ventilative cooling through automated window opening control systems to address thermal discomfort risk during the summer period: Framework, simulation and parametric analysis. Energy and Buildings, 153: 18–30.
Rokach L, Maimon O (2008). Data mining with Decision Trees: Theory and Applications. Singapore: World Scientific.
Roy R, Hinduja S, Teti R (2008). Recent advances in engineering design optimisation: Challenges and future trends. CIRP Annals, 57: 697–715.
Satish U, Mendell MJ, Shekhar K, Hotchi T, Sullivan D, Streufert S, Fisk WJ (2012). Is CO2 an indoor pollutant? Direct effects of low-to-moderate CO2 concentrations on human decision-making performance. Environmental Health Perspectives, 120: 1671–1677.
Sharmin T, Gul M, Al-Hussein M (2017). A user-centric space heating energy management framework for multi-family residential facilities based on occupant pattern prediction modeling. Building Simulation, 10: 899–916.
Tian Z, Zhang X, Jin X, Zhou X, Si B, Shi X (2018). Towards adoption of building energy simulation and optimization for passive building design: A survey and a review. Energy and Buildings, 158: 1306–1316.
Wang W, Rivard H, Zmeureanu RG (2003). Optimizing building design with respect to life-cycle environmental impacts. In: Proceedings of the 8th International IBPSA Building Simulation Conference, Eindhoven, the Netherlands, pp. 1355–1362.
Wright JA, Brownlee A, Mourshed MM, Wang M (2014). Multiobjective optimization of cellular fenestration by an evolutionary algorithm. Journal of Building Performance Simulation, 7: 33–51.
Xu X, Wang S, Sun Z, Xiao F (2009). A model-based optimal ventilation control strategy of multi-zone VAV air-conditioning systems. Applied Thermal Engineering, 29: 91–104.
Xu W, Chong A, Karaguzel OT, Lam KP (2016). Improving evolutionary algorithm performance for integer type multi-objective building system design optimization. Energy and Buildings, 127: 714–729.
Zhang Y, Wang J, Hu F, Wang Y (2017). Comparison of evaluation standards for green building in China, Britain, United States. Renewable and Sustainable Energy Reviews, 68: 262–271.
Acknowledgements
This paper is financially supported by the Ministry of Science and Technology of China (Project number: 2016YFC0700102), Scientific Research Foundation of Graduate School of Southeast University (YBJJ1801).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Tian, Z., Si, B., Wu, Y. et al. Multi-objective optimization model predictive dispatch precooling and ceiling fans in office buildings under different summer weather conditions. Build. Simul. 12, 999–1012 (2019). https://doi.org/10.1007/s12273-019-0543-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12273-019-0543-3