建筑室内环境建模、控制与优化及能耗预测
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摘要
当今,能源危机和环境污染是世界各国面临的共同挑战。我国是能源消耗大国,随着经济的飞速发展,建筑能耗已占社会总能耗的四分之一以上,且这一比例还在逐年升高。在此背景下,建筑节能技术越来越受到重视。从控制学角度看,建筑能量系统可看作多变量、非线性的复杂系统,建筑节能目标的实现涉及到建筑环境的优化控制、建筑能量的预测与管理等诸多方面内容。本文立足控制学科,在前人的基础上,从建筑环境控制与能量管理等相关领域入手,进行了如下研究工作。
·出于节能和室内环境优化的目的,暖通空调系统对热环境的控制与优化需要室内温度分布的动态信息帮助决策。通常,计算流体力学(CFD)模型能提供这样的精确信息,但是由于其迭代计算繁杂、耗时长,难以满足实时性要求。本文引入基于本征正交分解(POD)的模型降阶技术,与CFD仿真结合提出一种新的建筑热环境建模方法,可同时满足热环境建模的精度和实时性要求。POD模型降阶是
种映射方法,配合离散化技术,该方法可将无限维的非线性复杂系统变为仅与POD模式系数相关的低阶线性系统。具体建模方法为：首先,利用CFD工具对建筑室内热环境进行动态仿真,在此期间用快照的方式采集动态温度场信息；其次,运用有限体积法对能量平衡方程进行空间和时间的离散化,并建立离散能量平衡方程的状态空间表达式：然后,运用POD方法对室内动态温度场进行降阶,并运用Galerkin映射将高阶能量方程投射到降阶子空间上,从而得到阶数十以内的低阶线性系统。在一个二维房间的仿真实验中,室内温度场的POD降阶模型得到与CFD仿真基本一致的瞬、稳态精度,而其阶数低至六阶,证明了该方法的有效性。
·设计一个基于POD降阶模型的室内温度精确控制系统。该温控系统的特点在于,运用“离线-在线”策略建立了室内温度场的动态降阶模型,以实时反馈热环境信息提高温控精度。降阶温度场的初始状态通过一个温度传感器和卡尔曼滤波器估计得到。本文分别设计了单神经元自适应PID控制器和模型预测控制器对该方法进行仿真验证。结果表明,在速度场不变等假设条件下,该温控系统可利用室内温度场信息精确控制各区域温度,具有提高热舒适度和降低能耗的潜力。
·针对目前建筑室内环境的优化策略大都忽视环境参数空间分布的问题,本文利用POD降阶技术在环境建模方面的快速性和准确性,运用多维插值和遗传算法,设计一种综合考虑室内热舒适度、室内空气质量(IAQ)及空调能耗的优化控制策略。其中,建筑室内环境参数,包括温度场、气流场、CO2浓度分布、及热舒适度分布等,先通过CFD仿真得到；然后利用POD方法重构上述参数分布的低阶变化空间。优化方法采用遗传算法,控制变量包括置换通风系统的送风温度和速度。优化目标涵盖系统能耗、室内热舒适度、IAQ、以及垂直温差等。在遗传算法的每次优化迭代中,通过POD参数空间内的多维插值快速求解候选控制变量对应的系统响应,确保了优化算法的实时性。一个办公室环境的优化仿真证实该方法的有效性。
·作为典型的数据驱动建模方法,在过去20年间,人工神经网络在建筑能耗预测领域应用广泛。本文结合自适应模糊推理系统(ANFIS)和遗传算法的各自特点提出一种新的建筑能耗预测方法,即GA-ANFIS方法。其中,ANFIS通过训练输入/输出数据自适应调整T-S模糊系统的隶属度函数参数和结论参数,遗传算法则对ANFIS中的模糊规则参数进行优化以帮助构造最优规则基。设计ANFIS的分级结构用于应对输入变量过多造成的维数灾难问题。利用美国采暖、制冷与空调工程师学会(ASHRAE)提供的建筑能耗数据对该方法进行验证。结果表明,该方法与神经网络方法相比其建模时间在同一尺度内,而预测精度最多可提高20%。
·本文利用GA-ANFIS方法分别对玉泉图书馆和杭州某酒店的电力能耗作预测实验。能耗数据均由浙大中控能耗监控系统实时采集,气象数据来自浙江省气象局官方资料。实验结果验证了仿真结论,GA-ANFIS方法可结合目前的建筑能量采集系统,应用于建筑未来能耗的预测与分析。
Nowadays, energy crisis and environmental pollution are common challenges that ev-ery country in the world should face. With the rapid development of economy, energy con-sumption in China is considerable. Building energy consumption have accounted for more than a quarter of the total social energy consumption and the ratio is still in increases year by year. Against this background, the building energy-saving technologies have received more and more attention. From the control point of view, building energy system can be seen as multivariate nonlinear complex system. To achieve the goal of building energy efficiency many problems are involved, such as optimal control of building environment, building energy prediction and management, etc.. Based on the control subjects, this dis-sertation focuses on the areas of building environment control and energy management, and the main contributions are concluded as follows.
●For energy saving and indoor environment optimization purpose, Heating Venti-lation Air Conditioning (HVAC) systems need indoor temperature field's dynamic feed-back information. Usually, this kind of data is provided by Computational Fluid Dynamics (CFD) models. However, the complexity and time-consuming of CFD methods can't sat-isfy the real-time requirement. This study proposes a new indoor thermal environment modeling method by introducing Proper Orthogonal Decomposition (POD) model reduc-tion technique, which can help satisfy the real-time and precision requirements simultane-ously. The POD model reduction falls in the category of projection methods, which can transfer the infinite dimensional nonlinear systems into low order linear ones with the co-operation of discretization techniques. The concrete implemention includes several steps. First, construct indoor thermal environment by CFD dynamic simulation, and in this pro-cess, sample temperature fields by snapshotting method; Second, apply the Finite Volume Method (FVM) for the discretization of the energy equation in time and space domains, and construct its state-space expression. Then, apply POD model reduction on the ob- tained thermal environment, and project the energy equation onto the subspace optimally in energy sense, which obtains reduced linear system less than ten order. A simulation experiment of a two dimensional room is applied to investigate the performances of the proposed method. Results demonstrate the approaching abilities of the surrogate model compared with the CFD-based simulations, and the order is reduced to six, which prove the method's effectiveness.
●Based on the obtained POD reduced model, a temperature control strategy is pro-posed. The main feature is that, a "offline-online" strategy is used to construct the reduced temperature field, which can help increase the control precision by real-time feedback ther-mal information. The initial state of the reduced-order model is estimated by a temperature sensor combined with a Kalman filter. To investigate the control method's performance, both the single neuron adaptive PID controller and the model predictive control strategy are used. Simulation results show that, with the constant velocity field hypothesis, the pro-posed temperature control strategy can precisely control tiny zones'temperature of the room, and thus has the potential of thermal comfort improvement and energy saving.
●Aiming at the "Perfectly mixed" assumption of indoor air in most environmental optimization strategies, and taking advantage of POD approaches'fast and high-resolution modeling features, this study develops an optimization scheme, which can improve the thermal comfort, Indoor Air Quality (IAQ) and energy efficiency in a balanced way. In this optimization scheme, all environmental parameters, such as temperature, airflow, CO2emission and Predicted Mean Vote (PMV) distributions are obtained by CFD simulations. Then, POD method is used to reconstruct the reduced parameter-spaces. Genetic algo-rithm (GA) is chosen as the optimization approach. The control variables include supply air temperature and velocity of the displacement ventilation system. The optimization ob-jective includes several environmental indexes, such as system energy consumption, indoor thermal comfort, IAQ, vertical temperature difference, etc., In each iteration of GA, multi-dimensional interpolation within the obtained parameter subspaces are used to fast obtain the system response according to each candidate control variable pair, which can guaran- tee the real-time performance of the proposed optimization scheme. A simulation based example demonstrates the method's effectiveness.
●As a typical data driven modeling method, Neural Networks (NNs) have been widely used in building energy forecasting area in the past20years. This study combines the features of adaptive network-based fuzzy inference system (ANFIS) and GA, and proposes a new prediction method, called GA-ANFIS. In this method, ANFIS adaptively tunes the T-S type fuzzy system's premise parameters and consequent parameters by data training. GA is used to optimize the rule-base's parameters in ANFIS. The hierarchical structure of ANFIS helps solve the curse-of-dimensionality problem. A prediction test is applied using building energy data provided by ASHRAE (American Society of Heating Refrigerating and Air conditioning Engineer) web site. Results show that, the GA-ANFIS method has the same modeling time scale compared with NNs, while the prediction precision can improve up to20%.
●The GA-ANFIS method is applied to forecast the building electric energy consump-tion of Yuquan Library and Marco Polo Holiday Hotel in Hangzhou. Energy data are pro-vided by Zhejiang Supcon Software Co., Ltd., and meteorological data are obtained from Meteorological Bureau of Zhejiang Province. The forecasting results illustrate the effec-tiveness of the proposed method.
·出于节能和室内环境优化的目的,暖通空调系统对热环境的控制与优化需要室内温度分布的动态信息帮助决策。通常,计算流体力学(CFD)模型能提供这样的精确信息,但是由于其迭代计算繁杂、耗时长,难以满足实时性要求。本文引入基于本征正交分解(POD)的模型降阶技术,与CFD仿真结合提出一种新的建筑热环境建模方法,可同时满足热环境建模的精度和实时性要求。POD模型降阶是
种映射方法,配合离散化技术,该方法可将无限维的非线性复杂系统变为仅与POD模式系数相关的低阶线性系统。具体建模方法为：首先,利用CFD工具对建筑室内热环境进行动态仿真,在此期间用快照的方式采集动态温度场信息；其次,运用有限体积法对能量平衡方程进行空间和时间的离散化,并建立离散能量平衡方程的状态空间表达式：然后,运用POD方法对室内动态温度场进行降阶,并运用Galerkin映射将高阶能量方程投射到降阶子空间上,从而得到阶数十以内的低阶线性系统。在一个二维房间的仿真实验中,室内温度场的POD降阶模型得到与CFD仿真基本一致的瞬、稳态精度,而其阶数低至六阶,证明了该方法的有效性。
·设计一个基于POD降阶模型的室内温度精确控制系统。该温控系统的特点在于,运用“离线-在线”策略建立了室内温度场的动态降阶模型,以实时反馈热环境信息提高温控精度。降阶温度场的初始状态通过一个温度传感器和卡尔曼滤波器估计得到。本文分别设计了单神经元自适应PID控制器和模型预测控制器对该方法进行仿真验证。结果表明,在速度场不变等假设条件下,该温控系统可利用室内温度场信息精确控制各区域温度,具有提高热舒适度和降低能耗的潜力。
·针对目前建筑室内环境的优化策略大都忽视环境参数空间分布的问题,本文利用POD降阶技术在环境建模方面的快速性和准确性,运用多维插值和遗传算法,设计一种综合考虑室内热舒适度、室内空气质量(IAQ)及空调能耗的优化控制策略。其中,建筑室内环境参数,包括温度场、气流场、CO2浓度分布、及热舒适度分布等,先通过CFD仿真得到；然后利用POD方法重构上述参数分布的低阶变化空间。优化方法采用遗传算法,控制变量包括置换通风系统的送风温度和速度。优化目标涵盖系统能耗、室内热舒适度、IAQ、以及垂直温差等。在遗传算法的每次优化迭代中,通过POD参数空间内的多维插值快速求解候选控制变量对应的系统响应,确保了优化算法的实时性。一个办公室环境的优化仿真证实该方法的有效性。
·作为典型的数据驱动建模方法,在过去20年间,人工神经网络在建筑能耗预测领域应用广泛。本文结合自适应模糊推理系统(ANFIS)和遗传算法的各自特点提出一种新的建筑能耗预测方法,即GA-ANFIS方法。其中,ANFIS通过训练输入/输出数据自适应调整T-S模糊系统的隶属度函数参数和结论参数,遗传算法则对ANFIS中的模糊规则参数进行优化以帮助构造最优规则基。设计ANFIS的分级结构用于应对输入变量过多造成的维数灾难问题。利用美国采暖、制冷与空调工程师学会(ASHRAE)提供的建筑能耗数据对该方法进行验证。结果表明,该方法与神经网络方法相比其建模时间在同一尺度内,而预测精度最多可提高20%。
·本文利用GA-ANFIS方法分别对玉泉图书馆和杭州某酒店的电力能耗作预测实验。能耗数据均由浙大中控能耗监控系统实时采集,气象数据来自浙江省气象局官方资料。实验结果验证了仿真结论,GA-ANFIS方法可结合目前的建筑能量采集系统,应用于建筑未来能耗的预测与分析。
Nowadays, energy crisis and environmental pollution are common challenges that ev-ery country in the world should face. With the rapid development of economy, energy con-sumption in China is considerable. Building energy consumption have accounted for more than a quarter of the total social energy consumption and the ratio is still in increases year by year. Against this background, the building energy-saving technologies have received more and more attention. From the control point of view, building energy system can be seen as multivariate nonlinear complex system. To achieve the goal of building energy efficiency many problems are involved, such as optimal control of building environment, building energy prediction and management, etc.. Based on the control subjects, this dis-sertation focuses on the areas of building environment control and energy management, and the main contributions are concluded as follows.
●For energy saving and indoor environment optimization purpose, Heating Venti-lation Air Conditioning (HVAC) systems need indoor temperature field's dynamic feed-back information. Usually, this kind of data is provided by Computational Fluid Dynamics (CFD) models. However, the complexity and time-consuming of CFD methods can't sat-isfy the real-time requirement. This study proposes a new indoor thermal environment modeling method by introducing Proper Orthogonal Decomposition (POD) model reduc-tion technique, which can help satisfy the real-time and precision requirements simultane-ously. The POD model reduction falls in the category of projection methods, which can transfer the infinite dimensional nonlinear systems into low order linear ones with the co-operation of discretization techniques. The concrete implemention includes several steps. First, construct indoor thermal environment by CFD dynamic simulation, and in this pro-cess, sample temperature fields by snapshotting method; Second, apply the Finite Volume Method (FVM) for the discretization of the energy equation in time and space domains, and construct its state-space expression. Then, apply POD model reduction on the ob- tained thermal environment, and project the energy equation onto the subspace optimally in energy sense, which obtains reduced linear system less than ten order. A simulation experiment of a two dimensional room is applied to investigate the performances of the proposed method. Results demonstrate the approaching abilities of the surrogate model compared with the CFD-based simulations, and the order is reduced to six, which prove the method's effectiveness.
●Based on the obtained POD reduced model, a temperature control strategy is pro-posed. The main feature is that, a "offline-online" strategy is used to construct the reduced temperature field, which can help increase the control precision by real-time feedback ther-mal information. The initial state of the reduced-order model is estimated by a temperature sensor combined with a Kalman filter. To investigate the control method's performance, both the single neuron adaptive PID controller and the model predictive control strategy are used. Simulation results show that, with the constant velocity field hypothesis, the pro-posed temperature control strategy can precisely control tiny zones'temperature of the room, and thus has the potential of thermal comfort improvement and energy saving.
●Aiming at the "Perfectly mixed" assumption of indoor air in most environmental optimization strategies, and taking advantage of POD approaches'fast and high-resolution modeling features, this study develops an optimization scheme, which can improve the thermal comfort, Indoor Air Quality (IAQ) and energy efficiency in a balanced way. In this optimization scheme, all environmental parameters, such as temperature, airflow, CO2emission and Predicted Mean Vote (PMV) distributions are obtained by CFD simulations. Then, POD method is used to reconstruct the reduced parameter-spaces. Genetic algo-rithm (GA) is chosen as the optimization approach. The control variables include supply air temperature and velocity of the displacement ventilation system. The optimization ob-jective includes several environmental indexes, such as system energy consumption, indoor thermal comfort, IAQ, vertical temperature difference, etc., In each iteration of GA, multi-dimensional interpolation within the obtained parameter subspaces are used to fast obtain the system response according to each candidate control variable pair, which can guaran- tee the real-time performance of the proposed optimization scheme. A simulation based example demonstrates the method's effectiveness.
●As a typical data driven modeling method, Neural Networks (NNs) have been widely used in building energy forecasting area in the past20years. This study combines the features of adaptive network-based fuzzy inference system (ANFIS) and GA, and proposes a new prediction method, called GA-ANFIS. In this method, ANFIS adaptively tunes the T-S type fuzzy system's premise parameters and consequent parameters by data training. GA is used to optimize the rule-base's parameters in ANFIS. The hierarchical structure of ANFIS helps solve the curse-of-dimensionality problem. A prediction test is applied using building energy data provided by ASHRAE (American Society of Heating Refrigerating and Air conditioning Engineer) web site. Results show that, the GA-ANFIS method has the same modeling time scale compared with NNs, while the prediction precision can improve up to20%.
●The GA-ANFIS method is applied to forecast the building electric energy consump-tion of Yuquan Library and Marco Polo Holiday Hotel in Hangzhou. Energy data are pro-vided by Zhejiang Supcon Software Co., Ltd., and meteorological data are obtained from Meteorological Bureau of Zhejiang Province. The forecasting results illustrate the effec-tiveness of the proposed method.
引文
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