scholarly journals Modeling the Smart Factory Manufacturing Products Characteristics: The Perspective of Energy Consumption

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
A.B.M. Salman Rahman ◽  
Myeongbae Lee ◽  
Jonghyun Lim ◽  
Yongyun Cho ◽  
Changsun Shin
Keyword(s):  
Energy Consumption ◽  
Moving Average ◽  
Energy Utilization ◽  
Smart Factory ◽  
Utilization Rate ◽  
Correlation Technique ◽  
Energy Usage ◽  
Great Chance ◽  
Autoregressive Integrated Moving Average ◽  
Consumption Rates

Economic progress is built on the foundation of energy. In the industrial sector, smart factory energy consumption analysis and forecasts are crucial for improving energy consumption rates and also for creating profits. The importance of energy analysis and forecasting in an industrial environment is increasing speedily. It is a great chance to provide a technical boost to smart factories looking to reduce energy usage and produce more profit through the control and optimization modeling. It is tough to analyze energy usage and make accurate estimations of industrial energy consumption. Consequently, this study examines monthly energy consumption to identify the discrepancy between energy usages and energy needs. It depicts the link between energy consumption, demand, and various industrial goods by pattern recognition. The correlation technique is utilized in this study to figure out the link between energy usage and the weight of various materials used in product manufacturing. Next, we use the moving average approach to calculate the monthly and weekly moving averages of energy usages. The use of data-mining techniques to estimate energy consumption rates based on production is increasingly prevalent. This study uses the autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) to compare the actual data with forecasting data curves to enhance energy utilization. The Root Mean Square Error (RMSE) performance evaluation result for ARIMA and SARIMA is 8.70 and 10.90, respectively. Eventually, the Variable Important technique determines the smart factory’s most essential product to enhance the energy utilization rate and obtain profitable items for the smart factory.

scholarly journals Analysis of total carbon emission of the logistics industry in Guangdong and solutions to emission reduction

2021 ◽  
Vol 245 ◽  
pp. 01020
Author(s):  
Aixia Xu ◽  
Xiaoyong Yang
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Energy Utilization ◽  
Total Carbon ◽  
Utilization Rate ◽  
Low Carbon ◽  
Logistics Industry ◽  
Direct Energy ◽  
Reduce Emissions

The input-output method is employed in this study to measure the total carbon emission of the logistics industry in Guangdong. The findings revealed that the carbon emission of direct energy consumption of the logistics industry in Guangdong is far above the actual carbon emissions, the second and third industries play a significant role in carbon emission of indirect energy consumption in the logistics industry in Guangdong. To reduce energy consumption and carbon emissions in Guangdong, it is not only important to control the carbon emissions in the logistics industry, but strengthen carbon emission detection in relevant industries, improve the energy utilization rate and reduce emissions in other industries, and move towards low-carbon sustainable development.

Evolution of the Transition to a World Driven by Renewable Energy

2010 ◽  
Vol 132 (2) ◽  
Author(s):  
Brian M. Fronk ◽  
Richard Neal ◽  
Srinivas Garimella
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Renewable Resource ◽  
The United States ◽  
Energy Utilization ◽  
Standard Of Living ◽  
Renewable Energy Resources ◽  
Renewable Power ◽  
Consumption Rates ◽  
Developed Nations

The world’s energy supplies will continue to be pressured as the population grows and the standard of living rises in the developing world. A move by the rest of the world toward energy consumption rates on par with the United States is most probably unsustainable. An examination of population trends, current energy utilization rates, and estimated reserves shows that a major worldwide transition to renewable resources is necessary in the next 100 years. This paper examines one possible scenario of how energy usage and renewable power generation must evolve during this time period. As the global standard of living increases, energy consumption in developing nations will begin to approach that of the developed world. A combination of energy conservation and efficiency improvements in developed nations will be needed to push the worldwide energy consumption to approximately 200 million Btu per person per year. Fossil fuel resources will be exhausted or become prohibitively expensive, necessitating the development of renewable energy resources. At this projected steady state population and energy consumption, the required contribution of each type of renewable resource can be calculated. Comparing these numbers to the current renewable capacities illustrates the enormous effort that must be made in the next century.

scholarly journals An Energy Conserving and Transmission Radius Adaptive Scheme to Optimize Performance of Energy Harvesting Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2885 ◽  
Author(s):  
Xin Ju ◽  
Wei Liu ◽  
Chengyuan Zhang ◽  
Anfeng Liu ◽  
Tian Wang ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Harvesting ◽  
Network Performance ◽  
Maximum Energy ◽  
Energy Utilization ◽  
Utilization Rate ◽  
Cost Of Energy ◽  
Energy Conserving ◽  
The Cost

In energy harvesting wireless sensor networks (EHWSNs), the energy tension of the network can be relieved by obtaining the energy from the surrounding environment, but the cost on hardware cannot be ignored. Therefore, how to minimize the cost of energy harvesting hardware to reduce the network deployment cost, and further optimize the network performance, is still a challenging issue in EHWSNs. In this paper, an energy conserving and transmission radius adaptive (ECTRA) scheme is proposed to reduce the cost and optimize the performance of solar-based EHWSNs. There are two main innovations of the ECTRA scheme. Firstly, an energy conserving approach is proposed to conserve energy and avoid outage for the nodes in hotspots, which are the bottleneck of the whole network. The novelty of this scheme is adaptively rotating the transmission radius. In this way, the nodes with maximum energy consumption are rotated, balancing energy consumption between nodes and reducing the maximum energy consumption in the network. Therefore, the battery storage capacity of nodes and the cost on hardware. Secondly, the ECTRA scheme selects a larger transmission radius for rotation when the node can absorb enough energy from the surroundings. The advantages of using this method are: (a) reducing the energy consumption of nodes in near-sink areas, thereby reducing the maximum energy consumption and allowing the node of the hotspot area to conserve energy, in order to prevent the node from outage. Hence, the network deployment costs can be further reduced; (b) reducing the network delay. When a larger transmission radius is used to transmit data in the network, fewer hops are needed by data packet to the sink. After the theoretical analyses, the results show the following advantages compared with traditional method. Firstly, the ECTRA scheme can effectively reduce deployment costs by 29.58% without effecting the network performance as shown in experiment analysis; Secondly, the ECTRA scheme can effectively reduce network data transmission delay by 44–71%; Thirdly, the ECTRA scheme shows a better balance in energy consumption and the maximum energy consumption is reduced by 27.89%; And lastly, the energy utilization rate is effectively improved by 30.09–55.48%.

Water Supply Pumping Station Energy Consumption Analysis and Energy Saving Research Based on the Energy Consumption Coefficient

2013 ◽  
Vol 724-725 ◽  
pp. 999-1004
Author(s):  
Jing Ying Fang ◽  
Zhi Peng Li ◽  
Fang Fang Chen ◽  
Yan Hui Chen
Keyword(s):  
Energy Consumption ◽  
Water Supply ◽  
Energy Saving ◽  
Average Energy ◽  
Energy Utilization ◽  
Utilization Rate ◽  
Pump Efficiency ◽  
Pumping System ◽  
Consumption Coefficient ◽  
Average Energy Consumption

From the view point of efficiency, the energy consumption coefficient was used as the energy utilization evaluation index in a water supply pumping system, and through the comparative analysis between the energy consumption coefficient and the pump efficiency, the past pump efficiency that was used as the economic index in the system could not completely and scientifically reflect the energy utilization rate of the system, while the energy consumption coefficient could intuitively reflect conveying unit volume of liquid consumption in a water supply pumping system. Based on the theory of energy consumption coefficient, the comprehensive analysis of different energy saving methods, energy saving space estimation, and average energy consumption coefficient calculation under variable conditions of the system in a water supply pumping system can give a specific guidance scheme of energy saving to the system, and it has a guidance and reference significance to the energy saving reform in the water supply pumping system.

Evolution of the Transition to a World Driven by Renewable Energy

2008 ◽  
Author(s):  
Brian M. Fronk ◽  
Richard Neal ◽  
Srinivas Garimella
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Renewable Resource ◽  
The United States ◽  
Energy Utilization ◽  
Standard Of Living ◽  
Renewable Energy Resources ◽  
Renewable Power ◽  
Consumption Rates ◽  
Developed Nations

The world’s energy supplies will continue to be pressured as population grows and the standard of living rises in the developing world. A move by the rest of the world towards energy consumption rates on par with the United States is most probably unsustainable. An examination of population trends, current energy utilization rates, and estimated reserves shows that a major worldwide transition to renewable resources is necessary in the next one hundred years. This paper examines one possible scenario of how energy usage and renewable power generation must evolve in this time period. As the global standard of living increases, energy consumption in developing nations will begin to approach those of the developed world. A combination of energy conservation and efficiency improvements in developed nations will be needed to push the worldwide energy consumption to 200 million BTU per person per year. Fossil fuel resources will be exhausted or become prohibitively expensive, necessitating the development of renewable energy resources. At this projected steady state population and energy consumption, the required contribution of each type of renewable resource can be calculated. Comparing these numbers to the current renewable capacities illustrate the enormous effort that must be made in the next century.

Predictive Analytics of Energy Usage by IoT-Based Smart Home Appliances for Green Urban Development

2022 ◽  
Vol 22 (2) ◽  
pp. 1-26
Author(s):  
Mohammad Shorfuzzaman ◽  
M. Shamim Hossain
Keyword(s):  
Energy Consumption ◽  
Urban Development ◽  
Predictive Models ◽  
Energy Demand ◽  
Smart Home ◽  
Predictive Analytics ◽  
Moving Average ◽  
Energy Usage ◽  
Total Energy Consumption ◽  
Home Appliances

Green IoT primarily focuses on increasing IoT sustainability by reducing the large amount of energy required by IoT devices. Whether increasing the efficiency of these devices or conserving energy, predictive analytics is the cornerstone for creating value and insight from large IoT data. This work aims at providing predictive models driven by data collected from various sensors to model the energy usage of appliances in an IoT-based smart home environment. Specifically, we address the prediction problem from two perspectives. Firstly, an overall energy consumption model is developed using both linear and non-linear regression techniques to identify the most relevant features in predicting the energy consumption of appliances. The performances of the proposed models are assessed using a publicly available dataset comprising historical measurements from various humidity and temperature sensors, along with total energy consumption data from appliances in an IoT-based smart home setup. The prediction results comparison show that LSTM regression outperforms other linear and ensemble regression models by showing high variability ( R 2 ) with the training (96.2%) and test (96.1%) data for selected features. Secondly, we develop a multi-step time-series model using the auto regressive integrated moving average (ARIMA) technique to effectively forecast future energy consumption based on past energy usage history. Overall, the proposed predictive models will enable consumers to minimize the energy usage of home appliances and the energy providers to better plan and forecast future energy demand to facilitate green urban development.

scholarly journals Prediction of the Energy Demand Trend in Middle Africa—A Comparison of MGM, MECM, ARIMA and BP Models

2019 ◽  
Vol 11 (8) ◽  
pp. 2436 ◽  
Author(s):  
Wang ◽  
Zhan ◽  
Li
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Moving Average ◽  
Autoregressive Integrated Moving Average ◽  
Research Level ◽  
Different Types ◽  
Curve Method ◽  
The Mean ◽  
Bp Neural Network Model ◽  
Absolute Percent Error

Africa has abundant energy resources, but African energy research level is relatively low. In response to this gap, this paper takes Middle Africa as an example to systematically predict energy demand to give support. In this paper, we utilize four models, metabolic grey model (MGM), modified exponential curve method (MECM), autoregressive integrated moving average (ARIMA) and BP neural network model (BP), to predict the energy consumption of Middle Africa in the next 14 years. Comparing four completely different types of predictive models can fully depict the characteristics of the predictive data and give an all-round analysis of the predicted results. These proposed models are applied to simulate Middle Africa’s energy consumption between 1994 and 2016 to test their accuracy. Among them, the mean absolute percent error (MAPE) of MGM, MECM, ARIMA and BP are 2.41%, 4.80%, 1.91%, and 0.88%. The results show that MGM, MECM, ARIMA, and BP presented in this paper can produce reliable forecasting results. Therefore, the four models are used to forecast energy demand in the next 14 years (2017–2030). Forecasts show that energy demand of Middle Africa will continue to grow at a rate of about 5.37%.

Feedback as an Energy Conservation Strategy

1978 ◽  
Vol 22 (1) ◽  
pp. 537-537 ◽  
Author(s):  
Clive Seligman
Keyword(s):  
Energy Consumption ◽  
Energy Conservation ◽  
Psychological Research ◽  
Conservation Strategy ◽  
Conservation Strategies ◽  
Energy Usage ◽  
Immediate Feedback ◽  
Consumption Rates ◽  
Reduction Of Energy Consumption ◽  
Actual Energy Consumption

A great deal of psychological research has suggested that giving immediate feedback to an individual on the effects of his actions enables him better to control his actions. The application of this idea to the reduction of energy consumption is clear. In general homeowners are motivated by cost and other pressures to reduce their home energy consumption. Therefore, if they are given daily feedback on their actual energy consumption, this ought to enable them to better control their consumption rates. Why should feedback have this effect? First, since most homeowners are unaware of the amount of energy they use (the monthly utility bill is not clear or detailed enough to be very helpful), feedback provides information about energy usage. Second, frequent feedback indicates the success of various attempted conservation strategies; it can lead the homeowner to discover and to maintain conservation habits.

scholarly journals Simulation-Based Genetic Algorithm towards an Energy-Efficient Railway Traffic Control

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Daniel Tuyttens ◽  
Hongying Fei ◽  
Mohand Mezmaz ◽  
Jad Jalwan
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Total Energy ◽  
Traffic Control ◽  
Energy Efficient ◽  
Energy Utilization ◽  
Utilization Rate ◽  
Total Energy Consumption ◽  
Railway Traffic ◽  
Mode Control

The real-time traffic control has an important impact on the efficiency of the energy utilization in the modern railway network. This study is aimed to develop an energy-efficient railway traffic control solution for any specified railway. In other words, it is expected to define suitable driving profiles for all the trains running within a specified period through the targeted network with an objective to minimize their total energy consumption. How to optimize the train synchronization so as to benefit from the energy regenerated by electronic braking is also considered in this study. A method based on genetic algorithm and empirical single train driving strategies is developed for this objective. Six monomode strategies and one multimode strategy are tested and compared with the four scenarios extracted from the Belgian railway system. The results obtained by simulation show that the multi-mode control strategy overcomes the mono-mode control strategies with regard to global energy consumption, while there is no firm relation between the utilization rate of energy regenerated by dynamic braking operations and the reduction of total energy consumption.

Sign in / Sign up

Export Citation Format

Share Document