Research on Integration Technology of Large-Scale Public Building Energy Detection System Based on OPC

2012 ◽  
Vol 182-183 ◽  
pp. 950-954
Author(s):  
Chang Tao Wang ◽  
Jing Hai Zhou ◽  
Zhong Hua Han
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Detection System ◽  
Energy Detection ◽  
Building Energy ◽  
Development Work ◽  
Public Building ◽  
Consumption Data ◽  
Opc Technology ◽  
Standard Interface

As developing and integrating energy consumption detection system become more and more difficult, OPC technology is used to simplify the system. The integration of system can be improved largely and the development work can be decreased through OPC standard interface. Firstly, this paper introduces relative knowledge about OPC technology, and then realizes hardware and software development of energy consumption detection system in Shenyang large-scale public buildings. The result indicates that OPC can simplify system greatly and energy consumption data can be detected at real-time.

scholarly journals A Framework for the Automatic Integration and Diagnosis of Building Energy Consumption Data

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1395
Author(s):  
Shuang Yuan ◽  
Zhen-Zhong Hu ◽  
Jia-Rui Lin ◽  
Yun-Yi Zhang
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Building Energy ◽  
Primary Energy ◽  
Human Society ◽  
Building Energy Consumption ◽  
Unified Framework ◽  
Information Models ◽  
Consumption Data ◽  
Building Information

Buildings account for a majority of the primary energy consumption of the human society, therefore, analyses of building energy consumption monitoring data are of significance to the discovery of anomalous energy usage patterns, saving of building utility expenditures, and contribution to the greater environmental protection effort. This paper presents a unified framework for the automatic extraction and integration of building energy consumption data from heterogeneous building management systems, along with building static data from building information models to serve analysis applications. This paper also proposes a diagnosis framework based on density-based clustering and artificial neural network regression using the integrated data to identify anomalous energy usages. The framework and the methods have been implemented and validated from data collected from a multitude of large-scale public buildings across China.

Research on Energy Consumption Quota for Public Buildings in Hebei Province

2013 ◽  
Vol 361-363 ◽  
pp. 231-234
Author(s):  
Shi Long Liu ◽  
Yue Qun Xu ◽  
De Sheng Ju
Keyword(s):  
Energy Consumption ◽  
Building Energy ◽  
Linear Regression Method ◽  
Public Buildings ◽  
Public Building ◽  
Multiple Linear Regression Method ◽  
Energy Auditing ◽  
Building Area ◽  
The Government ◽  
Actual Energy Consumption

Based on 107 data of public building energy auditing and energy consumption statistics, using multiple linear regression method, this paper given an equation for calculating energy public building consumption quota. It can get energy consumption quota simply and conveniently. The equation was close to actual energy consumption of public buildings. It consider building area, heating degree day (HDD) and building type. The results can be help the government formulate the energy consumption quota for public buildings.

scholarly journals Analysis of Office Rooms Energy Consumption Data in Respect to Meteorological and Direct Sun Exposure Conditions

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7590
Author(s):  
Adam Kula ◽  
Albert Smalcerz ◽  
Maciej Sajkowski ◽  
Zygmunt Kamiński
Keyword(s):  
Energy Consumption ◽  
Public Service ◽  
Residential Buildings ◽  
Sun Exposure ◽  
Building Energy ◽  
The West ◽  
Building Energy Management ◽  
Consumption Data ◽  
Office Rooms ◽  
The Impact

There are many papers concerning the consumption of energy in different buildings. Most describe residential buildings, with only a few about office- or public service buildings. Few articles showcase the use of energy consumption in specific rooms of a building, directed in different geographical directions. On the other hand, many publications present methods, such as machine learning or AI, for building energy management and prediction of its consumption. These methods have limitations and represent a certain level of uncertainty. In order to compare energy consumption of different rooms, the measurements of particular building-room parameters were collected and analyzed. The obtained results showcase the effect of room location, regarding geographical directions, for the consumption of energy for heating. For south-exposed rooms, due to sun radiation, it is possible to switch heating off completely, and even overheating of 3 °C above the 22 °C temperature set point occurs. The impact of the sun radiation for rooms with a window directed east or west reached about 1 °C and lasts for a few hours before noon for the east, and until late afternoon for the west.

Perception and Management System for Building Energy Consumption Based on Internet of Things Technology

2013 ◽  
Vol 291-294 ◽  
pp. 945-948 ◽  
Author(s):  
Feng Qin Yu ◽  
Bei Tian ◽  
Xin Zhang ◽  
Qiang Wang ◽  
Dan Shi Yu ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Internet Of Things ◽  
Management System ◽  
Building Energy ◽  
Transport Layer ◽  
Building Energy Efficiency ◽  
Building Energy Consumption ◽  
Consumption Data ◽  
Internet Of Things Technology

The building energy consumption is one of three in China's energy consumption, the detection and monitoring for energy consumption of building is the basis for the work of building energy efficiency. This article describes a perception, monitoring and management system of building energy consumption based on Internet of Things technology architecture, in the system, various energy instrumentation is installed inside the building and measurement all kinds of energy consumption data in the perception layer, collection daterminal data connected to the RS485 bus access gateway for data transmission via Ethernet or mobile communication network in the network layer and transport layer, deal with the statistical analysis of the energy consumption data in the application layer. The system has been successfully applied to more than 50 large-scale public building to implement energy consumption monitoring and management, and the support of the underlying data for building energy efficiency.

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