一种基于高斯混合模型的高校中央空调计量方法

doi:10.16180/j.cnki.issn1007-7820.2020.10.006

Abstract

Abstract:

With the large scale use of central air conditioners in colleges, accurate measurement and reasonable billing of central air conditioners has become an increasingly concerned issue for administrators and students. In order to solve the conflicts of interests between managers and users, a college dormitory’s central air conditioning measurement method based on Gaussian mixture model is proposed in this study. The method is based on the load characteristics of central air conditioning in college student dormitory, and uses Gaussian mixture model to analyze the probability and statistical characteristics of air conditioning load. By measuring the electricity consumption of central air conditioning and terminal fans, the calculus of time differences method based on its statistical characteristic is adopted to achieve split charging. The proposed method not only realize accurate metering and charge, but also combines the intelligent power management system to make the electricity process more transparent and has certain energy-saving effects.

Key words: central air conditioning, metering system, Gaussian mixture model, Gaussian difference method, reliability analysis, split charging

CLC Number:

TP391

QI Xin,ZHANG Li,WANG Fuzhong,WANG Xiaohui. A Central Air Conditioning Measurement Method Based on Gaussian Mixture Model[J].Electronic Science and Technology, 2020, 33(10): 33-39.

Figures/Tables 9

Figure 1.

Figure 2.

Figure 3.

Table 1

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Table 2

References 20

[1]	孙连营, 王理. 建筑行业智慧建筑技术路线图研究[J]. 建筑科学, 2016,32(5):121-125.
	Sun Lianying, Wang Li. Research on technology roadmap of smart buildings in the building industry[J]. Building Science, 2016,32(5):121-125.
[2]	郑贵林, 张丽, 迟进武. “互联网+”建筑能源管控系统设计与实现[J]. 自动化与仪表, 2015,30(12):1-5,19.
	Zheng Guilin, Zhang Li, Chi Jinwu. Design and implementation of energy management and control system for buildings under the "Internet Plus" background[J]. Electrical Measurement & Instrumentation, 2015,30(12):1-5,19.
[3]	黄家骏. 高透型Low-E玻璃节能效果的模拟应用[J]. 河南理工大学学报(自然科学版), 2013,32(5):639-642.
	Huang Jiajun. The simulation of applied research on the energy-saving effect of high permeability type Low-E glass[J]. Journal of Henan Polytechnic University(Natural Science), 2013,32(5):639-642.
[4]	王蓓蓓, 朱峰, 嵇文路, 等. 中央空调降负荷潜力建模及影响因素分析[J]. 电力系统自动化, 2016,40(19):44-52.
	Wang Beibei, Zhu Feng, Ji Wenlu, et al. Load cutting potential modeling of central air-conditioning and analysis on influencing factors[J]. Automation of Electric Power Systems, 2016,40(19):44-52.
[5]	方修睦, 刘仕宽. 居住建筑供暖热费的分摊问题[J]. 暖通空调, 2007(5):51-53.
	Fang Xiumu, Liu Shikuan. Some issues about heat cost allocation in residential buildings[J]. Heating Ventilating & Air Conditioning, 2007(5):51-53.
[6]	张朝伟, 郄艳敏. 商务写字楼中央空调系统计费方式调研分析[J]. 建筑热能通风空调, 2018,37(8):51-53.
	Zhang Chaowei, Qie Yanmin. Investigation and analysis of billing mode of central air conditioning system in commercial office building[J]. Building Energy & Environment, 2018,37(8):51-53.
[7]	刘国文, 徐意. 基于RS-485总线的中央空调冷量计费系统[J]. 轻工机械, 2013,31(2):64-66.
	Liu Guowen, Xu Yi. Cooling metering and charging system of the central air-conditioning based on RS-485 bus[J]. Light Industry Machinery, 2013,31(2)64-66.
[8]	徐意, 沈旭东. 基于8051软核的中央空调计费系统的优化设计[J]. 节能技术, 2015,33(3):275-278.
	Xu Yi, Shen Xudong. Optimization design of charging system of central air-conditioning based on 8051 soft-core[J]. Energy Conservation Technology, 2015,33(3):275-278.
[9]	邢丽娟, 杨世忠. 基于空气焓值的变风量空调冷量计量[J]. 中国测试, 2013,39(4):18-21.
	Xing Lijuan, Yang Shizhong. Cool capacity metering based on air enthalpy in VAV air conditioning system[J]. China Measurement & Test, 2013,39(4):18-21.
[10]	Bai X, Zhang Z. Study on the cooling metering of fan coil units in central air conditioning systems[C]. Nanjing: International Conference on Remote Sensing, Environment and Transportation Engineering, 2011.
[11]	刘汉伟, 徐晓宁, 陈柳枝. 基于按冷量计费的中央空调分户计费系统的应用探讨[J]. 制冷与空调, 2012,12(4):36-38.
	Liu Hanwei, Xu Xiaoning, Chen Liuzhi. Discussion on application of household-based cooling capacity charging system for central air-conditioning[J]. Refrigeration and Air-Conditioning, 2012,12(4):36-38.
[12]	徐晓宁, 丁云飞, 朱赤辉. 基于网络控制的中央空调运行管理、控制与分户计费系统[J]. 建筑科学, 2009,25(6):65-67,72.
	Xu Xiaoning, Ding Yunfei, Zhu Chihui. Network control-based operational management, control and household-based charging system for central air conditioners[J]. Building Science, 2009,25(6):65-67,72.
[13]	王宇鹏. 城市道路随机车辆振动荷载谱研究[D]. 广州:广东工业大学, 2017.
	Wang Yupeng. Study on vibration load spectrum of random vehicle running on urban road[D]. Guangzhou:Guangdong University of Technology, 2017.
[14]	周莉鸿. 改进的混合高斯模型运动目标检测算法[J]. 电子科技, 2017,30(7):21-24.
	Zhou Lihong. Moving target detection algorithm based on the improved gaussian mixture model[J]. Electronic Science and Technology, 2017,30(7):21-24.
[15]	李辉. 基于峰值特征高斯混合建模的SAR目标识别[J]. 电子测量与仪器学报, 2018,32(8):103-108.
	Li Hui. SAR target recognition based on Gaussian mixture modeling of peak features[J]. Journal of Electronic Measurement and Instrumentation, 2018,32(8):103-108.
[16]	安曦宁. 基于改进混合高斯模型的人群密度估计研究[J]. 电子科技, 2017,30(5):180-183.
	An Xining. Crowd density estimation based on improved gaussian model[J]. Electronic Science and Technology, 2017,30(5):180-183.
[17]	张婧婧, 李勇伟. 基于超声波的多元线性回归测量模型研究[J]. 电子科技, 2017,30(7):58-61.
	Zhang Jingjing, Li Yongwei. Research on the model of multiple linear regression basedon ultrasonic[J]. Electronic Science and Technology, 2017,30(7):58-61.
[18]	李佩, 牛建立, 刘再斌. 灰色关联理论和回归分析在区域注浆改造中的应用[J]. 煤炭技术, 2017,36(12):148-150.
	Li Pei, Niu Jianli, Liu Zaibin. Application of grey relevancy theory and regression analysis method in regional grouting transformation[J]. Coal Technology, 2017,36(12):148-150.
[19]	张粒子, 扶柠柠, 王春丽, 等. 基于回归分析的电网运行维护费合理性评估[J]. 电力系统自动化, 2014,38(13):140-144. doi: 10.7500/AEPS20130912013
	Zhang Lizi, Fu Ningning, Wang Chunli, et al. Rationality evaluation of power grid operation and maintenance costs based on regression analysis[J]. Automation of Electric Power Systems, 2014,38(13):140-144. doi: 10.7500/AEPS20130912013
[20]	崔杨, 杨海威, 李鸿博. 基于高斯混合模型的风电场群功率波动概率密度分布函数研究[J]. 电网技术, 2016,40(4):1107-1112. doi: 10.13335/j.1000-3673.pst.2016.04.019
	Cui Yang, Yang Haiwei, Li Hongbo. Probability density distribution function of wind power fluctuation of a wind farm group based on the gaussian mixture model[J]. Power System Technology, 2016,40(4):1107-1112. doi: 10.13335/j.1000-3673.pst.2016.04.019

时间	用电量均值/kW·h	用电量标准差
1时~9时	0.024	0.032
10时~14时	0.055	0.052
15时~19时	0.059	0.049
20时~24时	0.104	0.090
中央空调机组	0.054	0.063

文献	方法	特点	月电费/元
[5]	面积分摊法	计量简单、方便,但易造成恶性消费	113.40
[13]	冷量计费法	理论上最准确的计量,但计量复杂,普适性差	90.80
本文方法	高斯差分计量法	计量准确,适用于工程实际	74.77

A Central Air Conditioning Measurement Method Based on Gaussian Mixture Model

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 20

Related Articles 15

Metrics

Comments

Recommended 0

[1]	ZHAO Yunfei, XUE Cunjin. A Remote Sensing Image Water Extraction Method by Combining Atrous Convolution and Pooling Models [J]. Electronic Science and Technology, 2025, 38(3): 40-46.
[2]	CHEN Yuyang, LI Feng. Integration of CNN and Transformer for Retinal OCT Image Fluid Segmentation Method [J]. Electronic Science and Technology, 2025, 38(3): 47-59.
[3]	SUN Jiaqian, ZHU Jinrong, ZHANG Xiaobao, ZHANG Yunkai, GONG Weijuan. Research on Iris Segmentation Algorithm Based on Ensemble Learning [J]. Electronic Science and Technology, 2025, 38(3): 88-94.
[4]	ZHENG Fangliang, WANG Yannian, LIAN Jihong, RUAN Pei. Face Image Super-Resolution Reconstruction Based on Conditional Priori Swin Transformer [J]. Electronic Science and Technology, 2025, 38(2): 35-41.
[5]	PAN Tianyi, SONG Yan. Nonlinear Representation Latent Factor Decomposition Model Based on Trust Relationship [J]. Electronic Science and Technology, 2025, 38(2): 53-61.
[6]	MA Zhuang, GAN Kaiyu, YIN Zhong. Emotion Recognition Based on Multimodal Fusion of the EEG and Peripheral Physiological Signals [J]. Electronic Science and Technology, 2025, 38(2): 62-69.
[7]	LAI Ying, JU Zhiyong, YE Yuxin. A Vehicle Detection Algorithm Based on Improved YOLOv4 [J]. Electronic Science and Technology, 2025, 38(1): 81-87.
[8]	LI Yunpeng, XI Zhihong. Dynamic Face Recognition System Design Based on RetinaFace and FaceNet [J]. Electronic Science and Technology, 2024, 37(12): 79-86.
[9]	MO Zhuorui, HUANG Qianghao, ZHANG Lin, CAO Yuqi, GE Weiting, YU Minghui. Grading and Diagnostic Method for Colorectal Cancer Immunohistochemical Images [J]. Electronic Science and Technology, 2024, 37(12): 24-31.
[10]	CUI Jingnan, HUANG Chunyan, LI Yanling. Improvement of YOLOv5s Algorithm for Steel Surface Defect Detection [J]. Electronic Science and Technology, 2024, 37(12): 48-55.
[11]	GAN Kaiyu, YIN Zhong. Emotion Recognition Method Based on EEG and Instantaneous Emotion Intensity Label [J]. Electronic Science and Technology, 2024, 37(11): 78-84.
[12]	XIA Qiangqiang, LI Feifei. Deep Learning with Noisy Labels Based on Co-Teaching [J]. Electronic Science and Technology, 2024, 37(11): 1-6.
[13]	CHENG Jinke, LI Gaolei. Privacy-Preserving Data Style Transfer Method for Artificial Intelligence of Things [J]. Electronic Science and Technology, 2024, 37(10): 1-5.
[14]	JU Ping, SONG Yan, ZHANG Yingjie, XU Yifu, SHAO Hang. Research on Agricultural Crop Diseases and Pests Classification Based on Masked Autoencoding [J]. Electronic Science and Technology, 2024, 37(10): 23-29.
[15]	FENG Ting, YING Jie, YANG Haima, LI Fang. Detection of Cervical Lesions Based on Multi-Scale Features and Attention Mechanism [J]. Electronic Science and Technology, 2024, 37(10): 30-39.