SEA  >> Vol. 6 No. 4 (August 2017)

    室内环境舒适度感知系统研究及实现
    Research on the Comfort Evaluation for Indoor Environment

  • 全文下载: PDF(1644KB) HTML   XML   PP.68-78   DOI: 10.12677/SEA.2017.64008  
  • 下载量: 116  浏览量: 155   国家自然科学基金支持

作者:  

罗 恒,倪启东:苏州科技大学 江苏省建筑智慧节能重点实验室,江苏 苏州;苏州市移动网络技术与应用重点实验室,江苏 苏州;苏州科技大学 电子与信息工程学院,江苏 苏州;
陈启蔚,邹优敏,陆家欣,王春霞,于 波:苏州科技大学 电子与信息工程学院,江苏 苏州

关键词:
热舒适度感知系统单片机FAHP算法无线蓝牙模块Thermal Comfort Perception System 51 microcomputer Fuzzy AHP Algorithm Wireless Bluetooth

摘要:

现代人类90%的生命周期在室内度过,因此,室内环境质量不仅影响到室内滞留人员的工作效率,也直接与其身体健康具有很高相关性。本文针对室内热舒适度评价机制不足的问题,依据FAHP(Fuzzy Analytic Hierarchy Process)热舒适度评价模型设计一种基于51单片机和蓝牙无线通信协议的室内环境热舒适度感知系统。系统用单片机、温湿度传感器、光强传感器采集环境参数并采用FAHP算法得到室内环境热舒适度测量值与标准舒适值的权重比,比值为1时表明当前环境为最舒适状态。系统包括软件系统和硬件系统。通过无线蓝牙模块将数据发送到电脑上的虚拟服务器以实现数据在个人网页上显示的功能。实验结果,系统能够将室内环境热舒适度量化,并给出舒适度排序结果。结果可用于室内建筑热环境评估,也可以为建筑节能提供理论依据。

It is reported that people spend 90% of their life indoors. Therefore, the indoor environment has great impact on both the working efficiency as well as health on the occupants. Based on the fuzzy AHP thermal comfort evaluation model, an indoor environment thermal comfort evaluation system based on 51 single chip microcomputer and Bluetooth wireless communication protocol is proposed in this paper to deal with the problem of lack of numeric evaluation in the indoor thermal comfort. The overall design of the system is described, including hardware design and software design. The system collects the environment parameters from the 51-chip through the sensors and uses the fuzzy AHP algorithm to process the data, and then sends the processed data to the virtual server on the computer through the wireless Bluetooth module to realize the function of the data displayed on the personal page. The difference between the comfort indexes measured in the site and the benchmark indicates the comfortable level of the sampling environment, the smaller the better. The most comfortable environment is achieved when the difference reaches 0. Experiment results show that the system can effectively detect the parameters of the indoor environment thermal comfort, and make a quantitative display of the comfort via the fuzzy AHP algorithm. The system has some practicality in the construction of intelligent, air conditioning and other fields.

文章引用:
罗恒, 陈启蔚, 邹优敏, 倪启东, 陆家欣, 王春霞, 于波. 室内环境舒适度感知系统研究及实现[J]. 软件工程与应用, 2017, 6(4): 68-78. https://doi.org/10.12677/SEA.2017.64008

参考文献

[1] Salata, F., Golasi, I., Vollaro, E.D.L., et al. (2015) Evaluation of Different Urban Microclimate Mitigation Strategies through a PMV Analysis. Sustainability, 7, 9012-9030.
https://doi.org/10.3390/su7079012
[2] Ciabattoni, L., Cimini, G., Ferracuti, F., et al. (2015) Indoor Thermal Comfort Control through Fuzzy Logic PMV Optimization. International Joint Conference on Neural Networks, Killarney, 12-17 July 2015, 1-6.
[3] 王鹏, 胡庆松, 姜波. 基于热舒适度的大型室内空间综合节能研究[J]. 环境工程, 2016, 26(1): 917-920.
[4] Gilani, S.I.U.H., Khan, M.H. and Pao, W. (2015) Thermal Comfort Analysis of PMV Model Prediction in Air Conditioned and Naturally Ventilated Buildings. Energy Procedia, 75, 1373-1379.
[5] 冯鑫, 段培永, 段晨旭. 基于PMV指标的室内环境热舒适度控制器设计[J]. 山东科学, 2016, 29(1): 110-115.
[6] Gao, J., Wang, Y. and Wargocki, P. (2015) Comparative Analysis of Modified PMV Models and SET Models to Predict Human Thermal Sensation in Naturally Ventilated Buildings. Building & Environment, 92, 200-208.
[7] Chang, Y.Y. and Lin, Y.P. (2016) PMV-Based Genetic Algorithms for Indoor Temperature Control System. 2016 International Symposium on Computer, Consumer and Control, Xi’an, 4-6 July 2016, 295-298.
https://doi.org/10.1109/IS3C.2016.84
[8] 罗一凡. 基于模糊自适应的室内热舒适度建模与控制[D]: [硕士学位论文]. 上海: 上海交通大学, 2015.
[9] 赵建华, 师振伟. 嵌入式Web服务器在智能家居控制系统的实现[J]. 计算机技术与发展, 2013(3): 164-167.
[10] 史成乾, 王鑫, 李岩昊, 周津锋, 温聪, 贾博文. 基于蓝牙通信的室内环境质量监控系统[J]. 信息通信, 2016(9): 185-187.
[11] Gouda, M.M., Danaher. S. and Underwood. C.P. (2001) Thermal Comfort Based Fuzzy Logic Controller. Building Service Engineering, 22, 237-253.
https://doi.org/10.1177/014362440102200403
[12] Kim, J.T., Ji, H.L., Sun, H.C., et al. (2015) Development of the Adaptive PMV Model for Improving Prediction Performances. Energy & Buildings, 98, 100-105.
https://doi.org/10.1016/j.enbuild.2014.08.051
[13] Zhu, Y., Ouyang, Q., Cao, B., et al. (2016) Dynamic Thermal Environment and Thermal Comfort. Indoor air, 26, 125- 137.
https://doi.org/10.1111/ina.12233
[14] Li, B., Li, W., Liu, H., et al. (2010) Physiological Expression of Human Thermal Comfort to Indoor Operative Temperature in the Non-HVAC Environment. Indoor & Built Environment, 19, 221-229.
https://doi.org/10.1177/1420326X10365213
[15] Homod, R.Z., Sahari, K.S.M., Almurib, H.A.F., et al. (2012) RLF and TS Fuzzy Model Identification of Indoor Thermal Comfort Based on PMV/PPD. Building & Environment, 49, 141-153.
https://doi.org/10.1016/j.buildenv.2011.09.012