室内环境品质模型预测控制与优化研究
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摘要
在现代建筑中,需对建筑室内环境品质进行有效的控制和优化来确保较高的舒适性和较低的能耗。室内环境品质包含了诸多不确定因素和非线性因素,传统的线性系统无法对其描述与控制。首先以西安建筑科技大学智能建筑实验室为研究对象,定义室内环境品质各物理参数和控制量的线性关系,建立基于双线性模型的室内环境品质控制与优化数学模型并进行验证分析。在此基础上,构建了基于模型预测的室内环境品质控制方法,并采用蚁群算法对其优化。实验结果显示,在相同室内外环境条件下,实验采集的室内环境品质参数与模型输出参数吻合度高,验证了模型的准确性;同时,在能耗较低的情况下,基于蚁群优化的模型预测控制能够快速稳定的跟踪室内温度和二氧化碳浓度设置点。
In modern architecture, it is necessary to control and optimize the quality of indoor environment to ensure the high comfort and low energy consumption. Indoor environmental quality that contains a lot of uncertainties and nonlinear factors is difficult to be described by the traditional linear system. This paper takes the intelligent building laboratory of Xi'an University of Architecture and Technology as the research object.Based on linear relationship between the physical parameters and control parameters of the indoor environmental quality, the control and energy consumption optimization modeling is established, using the bilinear model according to the data measured. On this basis, the method of model predictive control is constructed and optimized by the ant colony algorithm. Experimental results show the effectiveness of the proposed approach.
In modern architecture, it is necessary to control and optimize the quality of indoor environment to ensure the high comfort and low energy consumption. Indoor environmental quality that contains a lot of uncertainties and nonlinear factors is difficult to be described by the traditional linear system. This paper takes the intelligent building laboratory of Xi'an University of Architecture and Technology as the research object.Based on linear relationship between the physical parameters and control parameters of the indoor environmental quality, the control and energy consumption optimization modeling is established, using the bilinear model according to the data measured. On this basis, the method of model predictive control is constructed and optimized by the ant colony algorithm. Experimental results show the effectiveness of the proposed approach.
引文
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[2]李百战,刘晶,姚瑞明.重庆地区冬季教室热环境调查分析[J].暖通空调,2007,37(5):115-117.Li B Z,Liu J,Yao R M.Investigation and analysis on classroom thermal environment in winter in Chongqing[J].Journal of HV&AC,2007,37(5):115-117.
[3]DAVID Q,MAYNE.Model predictive control:Recent developments and future promise[J].Automatica,2014,50(12):2967-2986.
[4]席裕康,李德伟,林姝.模型预测控制-现状与挑战[J].自动化学报,2013,39(3):222-236.Xi Y K,Li D W,Lin S.Model Predictive Control-Status and Challenges[J].Acta Automatica Sinica,2013,39(3):222-236.
[5]PERVEZ H,NURSYAR B,PERUMAL N,et al.A review on optimized control systems for building energy and comfort management of smart sustainable buildings[J].Renewable and Sustainable Energy Reviews,2014,34(3):409-429
[6]ABDUL A,FARRONKH J.Theory and applications of HVACcontrol systems a review of model predictive control(MPC)[J].Building and Environment,2014,72:343-355.
[7]DALAMAGIDIS K,KOLOKOTSA D,KALAITZAKIS K,et al.Reinforcement learning for energy conservation and comfort in buildings[J].Building and Environment,2007,42(7):2686-98.
[8]KARATASOU S,SANTAMOURIS M,KARATASOU S,et al.Modeling and predicting buildings energy use with artificial neural networks:methods and results[J].Energy and Buildings,2006,38(8):949-58.
[9]GEROS V.Modeling and predicting buildings energy use with artificial neural networks:methods and results[J].Energy and Buildings,2006,38(8):949-58.
[10]VM Zavala.Real-Time Optimization Strategies for Building Systems[J].Industrial&Engineering Chemistry Research,2013,52(9):3137-3150.
[11]Justin R,Brandon M.Model predictive HVAC control with online occupancy model[J].Energy and Buildings,2014,82:675-684.
[12]Morosan P,Bourdais R,Dumur D,et al.Building temperature regulation using a distributed model predictive control[J].Energy Build,2010,42(9):1445-1452.
[13]EKMAN M.Modeling and control of bilinear systems,applications to theactivated sludge process[D].Uppsala:Uppsala University,2005.
[14]Liu Chu-an.Distribution theory of the least squares averaging estimator[J].Journal of Econometrics,2015,186(1):142-159.
[15]Bououden S,Chadli M,Karimi H.R.An ant colony optimization-based fuzzy predictive control approach for nonlinear processes[J].Information Sciences,2015,299(3):143-158.
[16]Ma Y,Borrelli F,Hencey B.et al,Model predictive control for the operation of building cooling systems[J].IEEE Trans.Control Syst.Technol.2012 20(3):796-803.
[17]Zhang X L,Chen X F,He Z J.An ACO-based algorithm for parameter optimization of support vector machines[J].Expert Syst.Appl.2010,37(9):6618-6628.
[18]Dorigo M,Maniezzo V,Colomi A,Ant system:an improved ant colony optimization and its application to vehicle routing problem with time windows[J].Neurocomputing.2012,98(3):101-107.