住宅建筑保障室内(热)环境质量的低能耗策略研究
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摘要
近十年来我国社会经济水平飞速发展,城乡居民的消费结构从“衣、食”逐步向“住、行”方向升级,生活从生存型向舒适型转变,对建筑面积、建筑室内环境舒适度等居住条件的要求逐渐提高,导致建筑能耗和资源消耗持续上升。如何在满足室内热环境质量的前提下,充分利用自然资源,采用各种气候适应性的调控技术,提出住宅建筑的低能耗策略(包括设计策略和运行策略),是建筑节能急待解决的难点之一。科学地寻找改善室内热环境的途径与方法,研究建筑气候适应性设计策略及调控技术,从而改善人民居住质量,降低建筑能耗以及促进人居环境的可持续发展,具有十分重要而深远的意义。
对于建筑设计,能源消耗和室内环境热舒适性是两个相互冲突的基本因素,要想在维持室内热环境的同时减少建筑能耗就得对这些因素进行综合优化,而且是一个多目标优化问题。因此,论文首先确定了住宅全年建筑能耗和室内适应性舒适状况为性能评价目标,以人工神经网络建立的预测模型为适应度函数,在遗传算法NAGAⅡ的基础上建立了建筑设计方案的多目标优化模型。
由于遗传算法在多目标优化过程中,针对每种设计方案都需要对住宅建筑全年能耗和室内舒适状况进行评价,若采用传统的动态模拟计算,在成千上万的迭代计算中,耗时将不可估量。因此,论文以神经网络方法构建的住宅建筑全年能耗和室内舒适状况的多目标快速预测模型为适应度评价函数。针对人工神经网络自身容易陷入局部最优解等问题进行了探讨,采用遗传算法对BP神经网络的连接权进行了优化,得到了更加准确实用的GA-BP神经网络用于住宅建筑全年能耗和室内舒适状况的预测;确定了GA-BP模型的输入神经元包括建筑平面布局、建筑方位、建筑体型系数等14个变量,模型的输出神经元参数为建筑单位面积的全年能耗和室内舒适状况2个。
其次,论文依据大量的调研数据,基于自动控制“黑箱理论”提出的aPMV热舒适调节模型理论,确定了重庆地区住宅建筑可接受的舒适区域,解决了本文设计与调控策略研究的一个基础性问题,即在满足何种舒适情况下研究建筑低能耗策略,确定了室内适应性热舒适的限定范围。
接着,以重庆市的典型建筑为例,论文以住宅建筑可接受舒适区域为限定条件,通过大量的模拟计算分析建筑在自然状态下各设计因素对室内适应性舒适状况的影响;同时分析了各设计因素对建筑全年能耗的影响,分别针对各单一的设计要素提出了节能设计建议。在此基础上,提供了144组模拟数据用于GA-BP多目标快速预测模型的训练与测试,训练与测试数据表明该模型能够较好的预测住宅建筑的能耗和室内舒适状况,有一定的应用价值。
将训练好的GA-BP多目标快速预测模型作为建筑设计方案的多目标优化模型的适应度评价函数,对基准建筑方案进行了多目标优化设计实例分析,提出了在维持室内热舒适状况下的节约建筑能耗的设计方案;通过对多目标优化模型的结果与能耗模拟软件的模拟结果比较分析可知本文所建立的多目标优化设计模型具有较高的准确性,可用于指导建筑师的方案设计。
最后,在节能优化设计方案的基础上,以可接受舒适区为约束条件,通过状态空间法计算分析得出了重庆地区住宅建筑通风技术、遮阳技术的分月调控方法;并通过对重庆市江某建筑室内热湿环境的实测,验证了通风调控、遮阳调控方法在实际住宅建筑中改善室内热环境的有效性,从测试结果表明,文中所提出的典型被动调控方法有一定的科学性和实用性。
论文的研究为建筑设计提供了以保证热舒适和低能耗要求为目的的具体分析方法和实用工具,并在此节能优化设计方法基础上,提出适用于特定气候与人体舒适性的建筑运行调控方法,实现被动和主动技术结合的方法,找到一条适合重庆地区住宅的低能耗调控策略,为此地区建筑节能技术体系的建立提供技术支撑。
With the rapid development of national residential construction, remarkable improvement of social economy and the increasing need of residents to get healthy and comfortable indoor environment in recent decades, energy efficiency now is especially urgent. How to make full use of natural resources and all kinds of climate adaptability control strategies on the premise of the acceptable thermal comfort of occupants and eventually coming up with low energy consumption control strategies for residential building has become one of the difficulties for energy efficiency. It has very important and profound significance to improve residents’living quality, reduce energy consumption and promote sustainable development for habitat environment by finding out ways and methods to improve indoor thermal environment, studying climate adaptability control techniques and design strategies and making prior use of natural resources.
Energy consumption and indoor thermal comfort are the two basic conflicting factors for building design. In order to keep indoor thermal comfort and reduce energy consumption at the same time, all these factors have to be made an overall and multiple targets optimizing. Therefore this paper has firstly made annual residential building energy consumption and indoor adaptability comfort as the goal of evaluated performance. And the paper chooses prediction model founded by artificial neural network as the fitness function and establishes multiple targets optimizing model of building design program on the foundation of genetic algorithm NAGAⅡ.
Every design scheme needs assessment function to evaluate residential building energy consumption and indoor comfort when using genetic algorithm on the process of multiple targets optimizing. And the time consumed in thousands of iterative computations is incalculable when using the conventional dynamic simulation method. Thus, this paper has taken the artificial neural network model of annual energy consumption and indoor comfort as the adaptability assessment function, discussed the issue that BP artificial neural network is easy to get into the problem of partial optimum solution, optimized the connection right of BP artificial neural network based on genetic algorithm and got the more accurate and practical GA-BP neural network used for prediction of annual residential building energy consumption and indoor comfort, determined the input neural parameters of GA-BP model including building floor plan, location, shape coefficient and other 11 variables and the output neural parameters are annual energy consumption per unit area and indoor comfort conditions.
Secondly, based on the aPMV thermal comfort control model proposed by automation control black box theory and numerous researched data, the paper defines the comfort zone of residential building in Chongqing, which solves a fundamental problem in the study of design and strategy control and gives a limitation to indoor adaptability thermal comfort. The fundamental problem is what kind of thermal comfort should be fulfilled when studying the low energy consumption strategy.
Then this research chose a typical building in Chongqing making the acceptable comfort zone of residential building as the limited condition and analyzed the influence of different kinds of design parameters on indoor comfort in natural environment with numerous simulations. At the same time, it also analyzed the influences of all kinds of design factors on annual energy consumption and proposed design suggestions of energy efficiency for every single design parameters. On this basis, 144 groups of simulation data are provided for trainings of GA-BP multiple targets prediction model.
The prediction model of residential building energy consumption and indoor comfort was trained and tested with the numerical simulation data of Energy Plus. And the tested data showed that this model which had certain application value could well predict the energy consumption and indoor comfort of residential buildings.
After making GA-BP neural network model of residential building energy consumption and indoor comfort as the adaptable assessment function, the paper made a case study of multiple targets optimization design towards basic building scheme, and then came up with an energy-saving design scheme which could keep the indoor environment thermal comfort. The multiple targets optimization design model has a relatively accuracy by comparing and analyzing its results and the results of the simulation software for energy consumption, which shows that the multiple targets optimization design model can guide architectures to make scheme design.
Finally, on the basis of energy efficiency optimization design program and with the limitation of acceptable comfort zone, this paper gets the monthly control methods of ventilation and shading technology in residential buildings in Chongqing by using state space method. After getting the typical passive control methods, the author measured the thermal environment in a building of Baisha Town, Jiangjin District in Chongqing and finally verified its effectiveness in real residential buildings. The results of measurement show that the typical passive control methods raised in this paper have certain scientific value and practicality.
The purpose of the thesis is to provide specific analytical method and practical tool for the requirement of thermal comfort for building design. And on the base of energy consumption optimization, this thesis has raised control methods for building management used for specific climate and human comfort which achieved the combination of passive and active technology, found out a low energy consumption control strategy fit for residential buildings in Chongqing, and provided technical support for the establishment of building energy-saving technology system in Chongqing.
对于建筑设计,能源消耗和室内环境热舒适性是两个相互冲突的基本因素,要想在维持室内热环境的同时减少建筑能耗就得对这些因素进行综合优化,而且是一个多目标优化问题。因此,论文首先确定了住宅全年建筑能耗和室内适应性舒适状况为性能评价目标,以人工神经网络建立的预测模型为适应度函数,在遗传算法NAGAⅡ的基础上建立了建筑设计方案的多目标优化模型。
由于遗传算法在多目标优化过程中,针对每种设计方案都需要对住宅建筑全年能耗和室内舒适状况进行评价,若采用传统的动态模拟计算,在成千上万的迭代计算中,耗时将不可估量。因此,论文以神经网络方法构建的住宅建筑全年能耗和室内舒适状况的多目标快速预测模型为适应度评价函数。针对人工神经网络自身容易陷入局部最优解等问题进行了探讨,采用遗传算法对BP神经网络的连接权进行了优化,得到了更加准确实用的GA-BP神经网络用于住宅建筑全年能耗和室内舒适状况的预测;确定了GA-BP模型的输入神经元包括建筑平面布局、建筑方位、建筑体型系数等14个变量,模型的输出神经元参数为建筑单位面积的全年能耗和室内舒适状况2个。
其次,论文依据大量的调研数据,基于自动控制“黑箱理论”提出的aPMV热舒适调节模型理论,确定了重庆地区住宅建筑可接受的舒适区域,解决了本文设计与调控策略研究的一个基础性问题,即在满足何种舒适情况下研究建筑低能耗策略,确定了室内适应性热舒适的限定范围。
接着,以重庆市的典型建筑为例,论文以住宅建筑可接受舒适区域为限定条件,通过大量的模拟计算分析建筑在自然状态下各设计因素对室内适应性舒适状况的影响;同时分析了各设计因素对建筑全年能耗的影响,分别针对各单一的设计要素提出了节能设计建议。在此基础上,提供了144组模拟数据用于GA-BP多目标快速预测模型的训练与测试,训练与测试数据表明该模型能够较好的预测住宅建筑的能耗和室内舒适状况,有一定的应用价值。
将训练好的GA-BP多目标快速预测模型作为建筑设计方案的多目标优化模型的适应度评价函数,对基准建筑方案进行了多目标优化设计实例分析,提出了在维持室内热舒适状况下的节约建筑能耗的设计方案;通过对多目标优化模型的结果与能耗模拟软件的模拟结果比较分析可知本文所建立的多目标优化设计模型具有较高的准确性,可用于指导建筑师的方案设计。
最后,在节能优化设计方案的基础上,以可接受舒适区为约束条件,通过状态空间法计算分析得出了重庆地区住宅建筑通风技术、遮阳技术的分月调控方法;并通过对重庆市江某建筑室内热湿环境的实测,验证了通风调控、遮阳调控方法在实际住宅建筑中改善室内热环境的有效性,从测试结果表明,文中所提出的典型被动调控方法有一定的科学性和实用性。
论文的研究为建筑设计提供了以保证热舒适和低能耗要求为目的的具体分析方法和实用工具,并在此节能优化设计方法基础上,提出适用于特定气候与人体舒适性的建筑运行调控方法,实现被动和主动技术结合的方法,找到一条适合重庆地区住宅的低能耗调控策略,为此地区建筑节能技术体系的建立提供技术支撑。
With the rapid development of national residential construction, remarkable improvement of social economy and the increasing need of residents to get healthy and comfortable indoor environment in recent decades, energy efficiency now is especially urgent. How to make full use of natural resources and all kinds of climate adaptability control strategies on the premise of the acceptable thermal comfort of occupants and eventually coming up with low energy consumption control strategies for residential building has become one of the difficulties for energy efficiency. It has very important and profound significance to improve residents’living quality, reduce energy consumption and promote sustainable development for habitat environment by finding out ways and methods to improve indoor thermal environment, studying climate adaptability control techniques and design strategies and making prior use of natural resources.
Energy consumption and indoor thermal comfort are the two basic conflicting factors for building design. In order to keep indoor thermal comfort and reduce energy consumption at the same time, all these factors have to be made an overall and multiple targets optimizing. Therefore this paper has firstly made annual residential building energy consumption and indoor adaptability comfort as the goal of evaluated performance. And the paper chooses prediction model founded by artificial neural network as the fitness function and establishes multiple targets optimizing model of building design program on the foundation of genetic algorithm NAGAⅡ.
Every design scheme needs assessment function to evaluate residential building energy consumption and indoor comfort when using genetic algorithm on the process of multiple targets optimizing. And the time consumed in thousands of iterative computations is incalculable when using the conventional dynamic simulation method. Thus, this paper has taken the artificial neural network model of annual energy consumption and indoor comfort as the adaptability assessment function, discussed the issue that BP artificial neural network is easy to get into the problem of partial optimum solution, optimized the connection right of BP artificial neural network based on genetic algorithm and got the more accurate and practical GA-BP neural network used for prediction of annual residential building energy consumption and indoor comfort, determined the input neural parameters of GA-BP model including building floor plan, location, shape coefficient and other 11 variables and the output neural parameters are annual energy consumption per unit area and indoor comfort conditions.
Secondly, based on the aPMV thermal comfort control model proposed by automation control black box theory and numerous researched data, the paper defines the comfort zone of residential building in Chongqing, which solves a fundamental problem in the study of design and strategy control and gives a limitation to indoor adaptability thermal comfort. The fundamental problem is what kind of thermal comfort should be fulfilled when studying the low energy consumption strategy.
Then this research chose a typical building in Chongqing making the acceptable comfort zone of residential building as the limited condition and analyzed the influence of different kinds of design parameters on indoor comfort in natural environment with numerous simulations. At the same time, it also analyzed the influences of all kinds of design factors on annual energy consumption and proposed design suggestions of energy efficiency for every single design parameters. On this basis, 144 groups of simulation data are provided for trainings of GA-BP multiple targets prediction model.
The prediction model of residential building energy consumption and indoor comfort was trained and tested with the numerical simulation data of Energy Plus. And the tested data showed that this model which had certain application value could well predict the energy consumption and indoor comfort of residential buildings.
After making GA-BP neural network model of residential building energy consumption and indoor comfort as the adaptable assessment function, the paper made a case study of multiple targets optimization design towards basic building scheme, and then came up with an energy-saving design scheme which could keep the indoor environment thermal comfort. The multiple targets optimization design model has a relatively accuracy by comparing and analyzing its results and the results of the simulation software for energy consumption, which shows that the multiple targets optimization design model can guide architectures to make scheme design.
Finally, on the basis of energy efficiency optimization design program and with the limitation of acceptable comfort zone, this paper gets the monthly control methods of ventilation and shading technology in residential buildings in Chongqing by using state space method. After getting the typical passive control methods, the author measured the thermal environment in a building of Baisha Town, Jiangjin District in Chongqing and finally verified its effectiveness in real residential buildings. The results of measurement show that the typical passive control methods raised in this paper have certain scientific value and practicality.
The purpose of the thesis is to provide specific analytical method and practical tool for the requirement of thermal comfort for building design. And on the base of energy consumption optimization, this thesis has raised control methods for building management used for specific climate and human comfort which achieved the combination of passive and active technology, found out a low energy consumption control strategy fit for residential buildings in Chongqing, and provided technical support for the establishment of building energy-saving technology system in Chongqing.
引文
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