scholarly journals PROVIDING HOUSEHOLD CUSTOMERS WITH SMART METERING DATA ON MOBILE DEVICES

2011 ◽  
pp. 344-351
Author(s):  
Syuzanna Hakobyan ◽  
Jan-Philipp Kohlbrecher ◽  
Johannes Pickert ◽  
Uwe Grossmann
Keyword(s):  
Energy Consumption ◽  
Mobile Devices ◽  
Carbon Dioxide Emissions ◽  
Data Access ◽  
Relevant Information ◽  
Energy Data ◽  
Consumption Behavior ◽  
Optimal Load ◽  
Optimum Price ◽  
Current Energy

One option for reducing carbon dioxide emissions is the future use of renewable instead of fossil resources. A consistent prerequisite is the optimization of household customers’ energy consumption behavior. The suitable visualization of energy data, corresponding costs and tariff information on mobile devices is essential to achieve this goal. Energy data are acquired by a smart meter and transferred over wireless networks to a mobile device to provide the customer with relevant information about his current energy usage. The household customer is hence enabled to manage his energy consumption, to hold the optimal load and meet the optimum price. Suitable typs of visualization, specific for different tariffs, are identified and presented together with necessary technologies and methods for communication and data access and delivery.

scholarly journals Mobility-Included DNN Partition Offloading from Mobile Devices to Edge Clouds

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 229
Author(s):  
Xianzhong Tian ◽  
Juan Zhu ◽  
Ting Xu ◽  
Yanjun Li
Keyword(s):  
Neural Networks ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Wireless Network ◽  
Deep Neural Networks ◽  
Mobile User ◽  
Computation Offloading ◽  
Long Latency ◽  
Total Latency ◽  
And Performance

The latest results in Deep Neural Networks (DNNs) have greatly improved the accuracy and performance of a variety of intelligent applications. However, running such computation-intensive DNN-based applications on resource-constrained mobile devices definitely leads to long latency and huge energy consumption. The traditional way is performing DNNs in the central cloud, but it requires significant amounts of data to be transferred to the cloud over the wireless network and also results in long latency. To solve this problem, offloading partial DNN computation to edge clouds has been proposed, to realize the collaborative execution between mobile devices and edge clouds. In addition, the mobility of mobile devices is easily to cause the computation offloading failure. In this paper, we develop a mobility-included DNN partition offloading algorithm (MDPO) to adapt to user’s mobility. The objective of MDPO is minimizing the total latency of completing a DNN job when the mobile user is moving. The MDPO algorithm is suitable for both DNNs with chain topology and graphic topology. We evaluate the performance of our proposed MDPO compared to local-only execution and edge-only execution, experiments show that MDPO significantly reduces the total latency and improves the performance of DNN, and MDPO can adjust well to different network conditions.

Energy Consumption in Discrete Part Production

2011 ◽  
Author(s):  
Devi K. Kalla ◽  
Samantha Corcoran ◽  
Janet Twomey ◽  
Michael Overcash
Keyword(s):  
Energy Consumption ◽  
Life Cycles ◽  
Manufacturing Processes ◽  
New Approach ◽  
Energy Data ◽  
Important Improvement ◽  
Drilling Unit ◽  
Cradle To Gate ◽  
Discrete Part ◽  
Manufacturing Machine

It is widely recognized that industrial production inevitably results in an environmental impact. Energy consumption during production is responsible for a part of this impact, but is often not provided in cradle-to-gate life cycles. Transparent description of the transformation of materials, parts, and chemicals into products is described herein as a means to improve the environmental profile of products and manufacturing machine. This paper focuses on manufacturing energy and chemicals/materials required at the machine level and provides a methodology to quantify the energy consumed and mass loss for simple products in a manufacturing setting. That energy data are then used to validate the new approach proposed by (Overcash et.al, 2009a, and 2009b) for drilling unit processes. The approach uses manufacturing unit processes as the basis for evaluating environmental impacts at the manufacturing phase of a product’s life cycle. Examining manufacturing processes at the machine level creates an important improvement in transparency which aids review and improvement analyses.

An Energy Efficient Estimation Algorithm for Wireless Sensor Network

2011 ◽  
Vol 216 ◽  
pp. 176-180
Author(s):  
Yong Ding ◽  
Yue Mei Su
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Estimation Algorithm ◽  
Efficient Estimation ◽  
Wireless Sensor ◽  
Estimation Algorithms ◽  
Current Energy

Wireless Sensor Networks functionality is closely related to network lifetime which depends on the energy consumption, so require energy- efficient protocols to improve the network lifetime. According to the analysis and summary of the current energy efficient estimation algorithms in wireless sensor network An energy-efficient algorithm is proposed,. Then this optimization algorithm proposed in the paper is adopted to improve the traditional diffusion routing protocol. Simulation results show that this algorithm is to effectively balance the network energy consumption, improve the network life-cycle and ensure the communication quality.

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.

scholarly journals Studying the Energy Consumption in Mobile Devices

2016 ◽  
Vol 94 ◽  
pp. 183-189 ◽  
Author(s):  
Mohammad Tawalbeh ◽  
Alan Eardley ◽  
Lo’ai Tawalbeh
Keyword(s):  
Energy Consumption ◽  
Mobile Devices

Impact of urbanization on per capita energy use and emissions in India

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Stuti Haldar ◽  
Gautam Sharma
Keyword(s):  
Carbon Dioxide ◽  
Energy Consumption ◽  
Population Density ◽  
Carbon Dioxide Emissions ◽  
Energy Demand ◽  
Energy Use ◽  
Energy Intensity ◽  
Series Data ◽  
Content Type ◽  
Per Capita

Purpose The purpose of this study is to investigate the impacts of urbanization on per capita energy consumption and emissions in India. Design/methodology/approach The present study analyses the effects of urbanization on energy consumption patterns by using the Stochastic Impacts by Regression on Population, Affluence and Technology in India. Time series data from the period of 1960 to 2015 has been considered for the analysis. Variables including Population, GDP per capita, Energy intensity, share of industry in GDP, share of Services in GDP, total energy use and urbanization from World Bank data sources have been used for investigating the relationship between urbanization, affluence and energy use. Findings Energy demand is positively related to affluence (economic growth). Further the results of the analysis also suggest that, as urbanization, GDP and population are bound to increase in the future, consequently resulting in increased carbon dioxide emissions caused by increased energy demand and consumption. Thus, reducing the energy intensity is key to energy security and lower carbon dioxide emissions for India. Research limitations/implications The study will have important policy implications for India’s energy sector transition toward non- conventional, clean energy sources in the wake of growing share of its population residing in urban spaces. Originality/value There are limited number of studies considering the impacts of population density on per capita energy use. So this study also contributes methodologically by establishing per capita energy use as a function of population density and technology (i.e. growth rates of industrial and service sector).

Joint Optimization Offloading Strategy of Execution Time and Energy Consumption of Mobile Edge Computing

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Qingzhu Wang ◽  
Xiaoyun Cui
Keyword(s):  
Energy Consumption ◽  
Mobile Devices ◽  
Execution Time ◽  
Optimal Solution ◽  
Computation Offloading ◽  
Joint Optimization ◽  
Random Strategy ◽  
Fitness Value ◽  
Cloud Server ◽  
Time And Energy

As mobile devices become more and more powerful, applications generate a large number of computing tasks, and mobile devices themselves cannot meet the needs of users. This article proposes a computation offloading model in which execution units including mobile devices, edge server, and cloud server. Previous studies on joint optimization only considered tasks execution time and the energy consumption of mobile devices, and ignored the energy consumption of edge and cloud server. However, edge server and cloud server energy consumption have a significant impact on the final offloading decision. This paper comprehensively considers execution time and energy consumption of three execution units, and formulates task offloading decision as a single-objective optimization problem. Genetic algorithm with elitism preservation and random strategy is adopted to obtain optimal solution of the problem. At last, simulation experiments show that the proposed computation offloading model has lower fitness value compared with other computation offloading models.

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