| 引用本文: | 简毅文,田园泉,高萌,刘晓霄,王旭.居住建筑夏季空调行为驱动特性的分析方法[J].哈尔滨工业大学学报,2018,50(10):175.DOI:10.11918/j.issn.0367-6234.201710050 |
| JIAN Yiwen,TIAN Yuanquan,GAO Meng,LIU Xiaoxiao,WANG Xu.Methodology of determining driving characteristics of air conditioning behaviors in summer[J].Journal of Harbin Institute of Technology,2018,50(10):175.DOI:10.11918/j.issn.0367-6234.201710050 |
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| 摘要: |
| 为挖掘和发现居住建筑夏季空调行为的驱动特性，依托北京城区某高校28户学生宿舍，开展室内热环境、空调开关状况以及空调行为驱动类型的问卷调查及现场实测，分析统计宿舍空调行为的驱动类型，采用建筑人行为动作模型获得各个宿舍在不同驱动类型下的空调行为概率曲线及其行为特征参数，并分析对比相同驱动类型下空调行为概率曲线的变化趋势，对各个宿舍进行分类，再对同类宿舍空调行为的平均概率进行拟合.研究表明：居住建筑空调行为驱动特性存在明显个体差异，体现在空调行为概率随环境驱动力变化的敏感性上，也即环境驱动力对不同类宿舍的空调行为呈现不同的驱动力度；依据概率变化趋势分类和采用平均概率拟合的方法，对各种驱动类型的空调行为统计归纳出2~4组不同的驱动力度，由此较好地反映出居住建筑夏季空调行为的群体特性.
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| 关键词: 空调行为 驱动特性 分析方法 特征参数 个体差异 |
| DOI：10.11918/j.issn.0367-6234.201710050 |
| 分类号:TU831 |
| 文献标识码:A |
| 基金项目:“十三五”国家重点研发计划(2016YFC0700501)；国家自然科学基金面上项目 ( 51278004) |
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| Methodology of determining driving characteristics of air conditioning behaviors in summer |
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JIAN Yiwen1,2,TIAN Yuanquan1,GAO Meng1,LIU Xiaoxiao1,WANG Xu1
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(1. College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China; 2. Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology(Beijing University of Technology), Beijing 100124, China)
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| Abstract: |
| To study the driving characteristics of air conditioning behavior, a field survey study was carried out in 28 dormitory rooms during the summer in 2016 in Beijing. Indoor temperature, humidity, occupants' presences at their rooms and their actions on air conditioners were measured. A questionnaire survey was conducted to investigate the driving forces of occupants' actions to turn on or turn off room air conditioners. The field survey data was grouped based on the driving forces to trigger air conditioning behaviors, and individual driving characteristics of air conditioning behavior were described as probability with driving force together with the characteristic parameters using an action-based behavior model. Moreover, driving characteristic parameters were analyzed and probability curves were subdivided under different driving forces. Two to four new probability curves were developed and the corresponding characteristic parameters were determined for each subdivision under different driving forces, which presented a quantitative description about driving characteristics of air conditioning level at group level. The results in this study are useful for further study of driving characteristics of air conditioning behavior on a large scale.
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| Key words: air conditioning behavior driving characteristic methodology characteristic parameters individual difference |
