E-Sketch: Gathering large-scale energy consumption data based on consumption patterns

2014 ◽  
Author(s):  
Zhichuan Huang ◽  
Hongyao Luo ◽  
David Skoda ◽  
Ting Zhu ◽  
Yu Gu
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Consumption Patterns ◽  
Consumption Data

Energy Consumption Simulation of the Air Conditioning System in Large-Scale Buildings

2013 ◽  
Vol 724-725 ◽  
pp. 1506-1509
Author(s):  
Guang Ming Zhang ◽  
Xue Shen ◽  
Gui Zhong Tang
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Air Conditioning ◽  
Large Scale ◽  
Working Environment ◽  
Operation Condition ◽  
Air Conditioning System ◽  
Factors Affecting ◽  
Network Training ◽  
Consumption Data

The working environment of air conditioning system in large-scale building is very complex, and there is no significant linear relationship between factors affecting energy consumption and energy demand of air conditioning system. This study adopts a nonlinear regression model: ANN (artificial neural network) model as energy model of air conditioning system. Take outdoor temperature, categorical day-of-week variable, equipment efficiency and terminal load as input, energy demand as output. Use energy consumption data in 2011 for network training, and energy consumption data in 2012 to verify the reliability of model. Based on energy analysis, the operation condition and the characteristics of energy consumption of air conditioning system for large-scale buildings in Nanjing could be precisely represented.

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 Cluster-Based Routing Protocol and Fault Detection for Wireless Sensor Network

2021 ◽  
Vol 13 (04) ◽  
pp. 71-83
Author(s):  
Slaheddine Chelbi ◽  
Riadh Moussi
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Routing Protocol ◽  
Wireless Sensors ◽  
Large Scale ◽  
Routing Algorithm ◽  
Data Accuracy ◽  
Wireless Sensor ◽  
Consumption Data ◽  
Sensor Devices

In Wireless Sensors Networks (WSN) based application, a large number of sensor devices must be deployed. Energy efficiency and network lifetime are the two most challenging issues in WSN. As a consequence, the main goal is to reduce the overall energy consumption using clustering protocols which have to ensure reliability and connectivity in large-scale WSN. This work presents a new clustering and routing algorithm based on the properties of the sensor networks. The main goal of this work is to extend the network lifetime via charge equilibration in the WSN. According to many errors with sensing devices and to have greater data accuracy, we use a quorum mechanism. The proposed algorithms are evaluated widely and the results are compared with related works. The experimental results show that the proposed algorithm provides an effective improvement in terms of energy consumption, data accuracy and network lifetime.

scholarly journals Energy audit and conservation potential analysis of a large comprehensive commercial building

2018 ◽  
Vol 22 (Suppl. 2) ◽  
pp. 567-576
Author(s):  
Chunzhi Zhang ◽  
Nianxia Yuan ◽  
Qianjun Mao
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Emission Reduction ◽  
Large Scale ◽  
Rapid Development ◽  
Recovery Period ◽  
Commercial Building ◽  
Energy Saving Potential ◽  
Consumption Data ◽  
Actual Energy Consumption

With the rapid development of large-scale public buildings, energy consumption has increased, of which the energy consumption of comprehensive commercial buildings can reach 10~20 times the common building energy consumption, and has great energy saving potential. In this paper, a large comprehensive commercial building in Chengdu is taken as an example to analyze the energy consumption through the actual energy consumption data, viewed from the energy-saving and emission-reduction and static investment payback period point. The results show that the energy saving rate of the building can be achieved by 32.64%, the emission reduction is 6196.52 t CO2 per year, and the investment recovery period is only about 0.90 years, which provides a reference for similar buildings.

Establishing Smartphone User Behavior Model Based on Energy Consumption Data

2021 ◽  
Vol 16 (2) ◽  
pp. 1-40
Author(s):  
Ming Ding ◽  
Tianyu Wang ◽  
Xudong Wang
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Feature Vector ◽  
User Behavior ◽  
Lower Layer ◽  
Training Data ◽  
Usage Pattern ◽  
User Behaviors ◽  
Consumption Data ◽  
Usage Patterns

In smartphone data analysis, both energy consumption modeling and user behavior mining have been explored extensively, but the relationship between energy consumption and user behavior has been rarely studied. Such a relationship is explored over large-scale users in this article. Based on energy consumption data, where each users’ feature vector is represented by energy breakdown on hardware components of different apps, User Behavior Models (UBM) are established to capture user behavior patterns (i.e., app preference, usage time). The challenge lies in the high diversity of user behaviors (i.e., massive apps and usage ways), which leads to high dimension and dispersion of data. To overcome the challenge, three mechanisms are designed. First, to reduce the dimension, apps are ranked with the top ones identified as typical apps to represent all. Second, the dispersion is reduced by scaling each users’ feature vector with typical apps to unit ℓ 1 norm. The scaled vector becomes Usage Pattern, while the ℓ 1 norm of vector before scaling is treated as Usage Intensity. Third, the usage pattern is analyzed with a two-layer clustering approach to further reduce data dispersion. In the upper layer, each typical app is studied across its users with respect to hardware components to identify Typical Hardware Usage Patterns (THUP). In the lower layer, users are studied with respect to these THUPs to identify Typical App Usage Patterns (TAUP). The analytical results of these two layers are consolidated into Usage Pattern Models (UPM), and UBMs are finally established by a union of UPMs and Usage Intensity Distributions (UID). By carrying out experiments on energy consumption data from 18,308 distinct users over 10 days, 33 UBMs are extracted from training data. With the test data, it is proven that these UBMs cover 94% user behaviors and achieve up to 20% improvement in accuracy of energy representation, as compared with the baseline method, PCA. Besides, potential applications and implications of these UBMs are illustrated for smartphone manufacturers, app developers, network providers, and so on.

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.

Interference mitigation in point to point wireless sensor networks using LSP protocol and time division multiplexing approach

2020 ◽  
Vol 39 (4) ◽  
pp. 5449-5458
Author(s):  
A. Arokiaraj Jovith ◽  
S.V. Kasmir Raja ◽  
A. Razia Sulthana
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Control Parameter ◽  
Interference Mitigation ◽  
Wireless Sensor ◽  
Point To Point ◽  
A Minor ◽  
Two Stages ◽  
Time Division ◽  
And Control

Interference in Wireless Sensor Network (WSN) predominantly affects the performance of the WSN. Energy consumption in WSN is one of the greatest concerns in the current generation. This work presents an approach for interference measurement and interference mitigation in point to point network. The nodes are distributed in the network and interference is measured by grouping the nodes in the region of a specific diameter. Hence this approach is scalable and isextended to large scale WSN. Interference is measured in two stages. In the first stage, interference is overcome by allocating time slots to the node stations in Time Division Multiple Access (TDMA) fashion. The node area is split into larger regions and smaller regions. The time slots are allocated to smaller regions in TDMA fashion. A TDMA based time slot allocation algorithm is proposed in this paper to enable reuse of timeslots with minimal interference between smaller regions. In the second stage, the network density and control parameter is introduced to reduce interference in a minor level within smaller node regions. The algorithm issimulated and the system is tested with varying control parameter. The node-level interference and the energy dissipation at nodes are captured by varying the node density of the network. The results indicate that the proposed approach measures the interference and mitigates with minimal energy consumption at nodes and with less overhead transmission.

scholarly journals An Improved Forecasting Method and Application of China’s Energy Consumption under the Carbon Peak Target

2021 ◽  
Vol 13 (15) ◽  
pp. 8670
Author(s):  
Xiwen Cui ◽  
Shaojun E ◽  
Dongxiao Niu ◽  
Dongyu Wang ◽  
Mingyu Li
Keyword(s):  
Sustainable Development ◽  
Energy Consumption ◽  
Environmental Pollution ◽  
Optimization Methods ◽  
Support Vector ◽  
The Sustainable Development ◽  
Consumption Data ◽  
Forecasting Method ◽  
Forecasting Performance ◽  
Carbon Peak

In the process of economic development, the consumption of energy leads to environmental pollution. Environmental pollution affects the sustainable development of the world, and therefore energy consumption needs to be controlled. To help China formulate sustainable development policies, this paper proposes an energy consumption forecasting model based on an improved whale algorithm optimizing a linear support vector regression machine. The model combines multiple optimization methods to overcome the shortcomings of traditional models. This effectively improves the forecasting performance. The results of the projection of China’s future energy consumption data show that current policies are unable to achieve the carbon peak target. This result requires China to develop relevant policies, especially measures related to energy consumption factors, as soon as possible to ensure that China can achieve its peak carbon targets.

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