scholarly journals A Novel Method for Detecting Abnormal Energy Data in Building Energy Monitoring System

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Liang Zhao
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Real Time ◽  
Difference Method ◽  
Building Energy ◽  
Energy Monitoring ◽  
Order Difference ◽  
Energy Data ◽  
First Order ◽  
Novel Method

This paper presents a novel abnormal data detecting algorithm based on the first order difference method, which could be used to find out outlier in building energy consumption platform real time. The principle and criterion of methodology are discussed in detail. The results show that outlier in cumulative power consumption could be detected by our method.

Continuous Monitoring, Modeling, and Evaluation of Actual Building Energy Systems

2014 ◽  
Author(s):  
Maxim L. Sankey ◽  
Sheldon M. Jeter ◽  
Trevor D. Wolf ◽  
Donald P. Alexander ◽  
Gregory M. Spiro ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Continuous Monitoring ◽  
Energy System ◽  
Building Energy ◽  
Energy Performance ◽  
Weather Data ◽  
Practical Procedure ◽  
Heating And Cooling ◽  
Institute Of Technology

Residential and commercial buildings account for more than 40% of U.S. energy consumption, most of which is related to heating, ventilation and air conditioning (HVAC). Consequently, energy conservation is important to building owners and to the economy generally. In this paper we describe a process under development to continuously evaluate a building’s heating and cooling energy performance in near real-time with a procedure we call Continuous Monitoring, Modeling, and Evaluation (CMME). The concept of CMME is to model the expected operation of a building energy system with actual weather and internal load data and then compare modeled energy consumption with actual energy consumption. For this paper we modeled two buildings on the Georgia Institute of Technology campus. After creating our building models, internal lighting loads and equipment plug-loads were collected through electrical sub-metering, while the building occupancy load was recorded using doorway mounted people counters. We also collected on site weather and solar radiation data. All internal loads were input into the models and simulated with the actual weather data. We evaluated the building’s overall performance by comparing the modeled heating and cooling energy consumption with the building’s actual heating and cooling energy consumption. Our results demonstrated generally acceptable energy performance for both buildings; nevertheless, certain specific energy inefficiencies were discovered and corrective actions are being taken. This experience shows that CMME is a practical procedure for improving the performance of actual well performing buildings. With improved techniques, we believe the CMME procedure could be fully automated and notify building owners in real-time of sub-optimal building performance.

Reliable data transmission method based on 6LoWPAN for building energy systems

2019 ◽  
Vol 41 (5) ◽  
pp. 623-633
Author(s):  
Cui-Min Li ◽  
Chun-Ying Li ◽  
Lei Wang
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Data Transmission ◽  
Building Energy ◽  
Reliable Data ◽  
Wireless Sensor ◽  
Building Energy Consumption ◽  
Transmission Method ◽  
Energy Monitoring ◽  
Transmission Reliability

The building energy internet of things is based on radio frequency technology and a wireless sensor network that can collect building energy consumption data in real time. However, with the increasing complexity of wireless sensor network topology, there is a problem of insufficient IP address space relying on IPv4 protocol. In this paper, a design scheme of a building energy system based on 6LoWPAN network is proposed. IPv4/IPv6 address conversion is used to realise the access of IP addresses to each other, so as to monitor building energy consumption information anytime and anywhere. In view of the shortcomings of existing wireless network data transmission methods in low energy consumption and high reliability in building energy monitoring applications, a reliable data transmission method based on multipath routing coding algorithm is proposed. This strategy improves the transmission reliability of the network by increasing the number of redundant packets, and reduces the energy consumption of the network by reducing the number of transmission paths. The simulation results show that the proposed method can effectively improve the success rate of data packet transmission, reduce the standard energy consumption of sensor networks, and provide an effective method for the application of wireless sensor networks in building energy monitoring systems. Practical application: This paper studies how to improve transmission reliability and energy efficiency in cluster-based WSN and proposes a multi-path transmission strategy for selective coding of intermediate cluster head nodes. The strategy improves the transmission reliability of the network by increasing the number of transmissions of redundant packets and reduces the network energy consumption by reducing the number of transmission paths. It has good use value for the actual development and application of the building energy consumption monitoring system.

Understanding the effects of environmental factors on building energy efficiency designs and credits

2017 ◽  
Vol 15 (03) ◽  
pp. 270-285 ◽  
Author(s):  
Jonghoon Kim ◽  
Jin-Young Hyun ◽  
Wai K. Chong ◽  
Samuel Ariaratnam
Keyword(s):  
Energy Consumption ◽  
Environmental Factors ◽  
Real Time ◽  
Building Energy ◽  
Energy Performance ◽  
Building Energy Efficiency ◽  
Time Data ◽  
Chi Square ◽  
Content Type ◽  
Real Time Data

Purpose The purpose of this study was to explore the relationship between environmental factors and building energy consumption of three Leadership in Energy and Environmental Design (LEED)-certified buildings at the Arizona State University, by establishing the relationships of the outside atmospheric temperature and the energy consumed in the building using real-time data generated from different sources. Design/methodology/approach K-means clustering analysis is used to calibrate and eliminate unwanted influences or factors from a set of building consumption real-time data. For further statistical analysis, the chi-square is used to verify if the results are ample to prove the findings. Findings Few studies have addressed building energy consumption real-time data versus LEED Energy and Atmosphere (EA) credits with the data mining technique (k-means clustering) on most of building performance analyses. This study highlighted that the calibrating energy data are a better approach to analyze energy use in buildings and that there is a relationship between LEED credits’ (EA) Optimize Energy Performance scores and building energy efficiency. However, the energy consumption data alone do not yield useful results to establish the cause and effect relationships. Originality/value Although there are several previous research studies regarding LEED building energy performance, this research study focused on the LEED building energy performance versus LEED EA credits versus environmental factors using real-time building energy data and various statistical methods (e.g. K-means clustering and chi-square). The findings provide researchers, engineers and architects with valuable references for building energy analysis methods and supplements in LEED standards.

scholarly journals Development and Application of a ZigBee-Based Building Energy Monitoring and Control System

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Changhai Peng ◽  
Kun Qian
Keyword(s):  
Control System ◽  
Energy Consumption ◽  
Energy Conservation ◽  
Building Energy ◽  
Base Station ◽  
Main Function ◽  
Monitoring And Control ◽  
Energy Monitoring ◽  
Monitoring And Control System ◽  
And Control

Increasing in energy consumption, particularly with the ever-increasing growth and development of urban systems, has become a major concern in most countries. In this paper, the authors propose a cost-effective ZigBee-based building energy monitoring and control system (ZBEMCS), which is composed of a gateway, a base station, and sensors. Specifically, a new hardware platform for power sensor nodes is developed to perform both local/remote power parameter measurement and power on/off switching for electric appliances. The experimental results show that the ZBEMCS can easily monitor energy usage with a high level of accuracy. Two typical applications of ZBEMCS such as subentry metering and household metering of building energy are presented. The former includes lighting socket electricity, HVAC electricity, power electricity and special electricity. The latter includes household metering according to the campus’s main function zone and each college or department. Therefore, this system can be used for energy consumption monitoring, long-term energy conservation planning, and the development of automated energy conservation for building applications.

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