A Power Monitoring System Based on a Multi-Component Power Model

2018 ◽  
Vol 10 (1) ◽  
pp. 16-30 ◽  
Author(s):  
Weiwei Lin ◽  
Haoyu Wang ◽  
Wentai Wu
Keyword(s):  
Energy Consumption ◽  
Coarse Grained ◽  
Average Error ◽  
Power Model ◽  
Hard Disks ◽  
Monitoring Methods ◽  
Power Estimate ◽  
Power Monitoring ◽  
Multiple Variables ◽  
Software Utility

As the increasing IT energy consumption emerged as a prominent issue, computer system energy consumption monitoring and optimization has gradually become a significant research forefront. However, most existing energy monitoring methods are limited to hardware-based measurement or coarse-grained energy consumption estimation. They cannot provide fine-grained energy consumption data (i.e., component energy consumption) and high-scalability for distributed cloud environments. In this article, the authors first study widely-used power models of CPUs, memory and hard disks. Then, following an investigation into disk power behaviors in sequential I/O and random I/O, they propose an improved I/O-mode aware disk power model with multiple variables and thresholds. They developed EnergyMeter, a monitoring software utility that can provide accurate power estimate by exploiting a multi-component power model. Experiments based on PCMark prove that the average error of EnergyMeter is merely 5% under a variety of workloads

scholarly journals Machine Learning-Based Models for the Estimation of the Energy Consumption in Metal Forming Processes

Metals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 833
Author(s):  
Irene Mirandola ◽  
Guido A. Berti ◽  
Roberto Caracciolo ◽  
Seungro Lee ◽  
Naksoo Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Finite Element ◽  
Energy Consumption ◽  
Metal Forming ◽  
Mutual Influence ◽  
Rolling Process ◽  
Ring Rolling ◽  
Gradient Boosting ◽  
Average Error ◽  
Ring Rolling Mill

This research provides an insight on the performances of machine learning (ML)-based algorithms for the estimation of the energy consumption in metal forming processes and is applied to the radial-axial ring rolling process. To define the mutual influence between ring geometry, process settings, and ring rolling mill geometries with the resulting energy consumption, measured in terms of the force integral over the processing time (FIOT), FEM simulations have been implemented in the commercial SW Simufact Forming 15. A total of 380 finite element simulations with rings ranging from 650 mm < DF < 2000 mm have been implemented and constitute the bulk of the training and validation datasets. Both finite element simulation settings (input), as well as the FI (output), have been utilized for the training of eight machine learning models, implemented with Python scripts. The results allow defining that the Gradient Boosting (GB) method is the most reliable for the FIOT prediction in forming processes, being its maximum and average errors equal to 9.03% and 3.18%, respectively. The trained ML models have been also applied to own and literature experimental cases, showing a maximum and average error equal to 8.00% and 5.70%, respectively, thus proving once again its reliability.

scholarly journals Spin Wave Based Approximate 4:2 Compressor

2021 ◽  
Author(s):  
Abdulqader Mahmoud ◽  
Frederic Vanderveken ◽  
Florin Ciubotaru ◽  
Christoph Adelmann ◽  
Said Hamdioui ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Error Rate ◽  
Energy Efficient ◽  
Directional Coupler ◽  
State Of The Art ◽  
The Other ◽  
Average Error ◽  
Majority Gate ◽  
Micromagnetic Simulations ◽  
Average Error Rate

In this paper, we propose an energy efficient SW based approximate 4:2 compressor comprising a 3-input and a 5-input Majority gate. We validate our proposal by means of micromagnetic simulations, and assess and compare its performance with one of the state-of-the-art SW, 45nm CMOS, and Spin-CMOS counterparts. The evaluation results indicate that the proposed compressor consumes 31.5\% less energy in comparison with its accurate SW design version. Furthermore, it has the same energy consumption and error rate as the approximate compressor with Directional Coupler (DC), but it exhibits 3x lower delay. In addition, it consumes 14% less energy, while having 17% lower average error rate than the approximate 45nm CMOS counterpart. When compared with the other emerging technologies, the proposed compressor outperforms approximate Spin-CMOS based compressor by 3 orders of magnitude in term of energy consumption while providing the same error rate. Finally, the proposed compressor requires the smallest chip real-estate measured in terms of devices.

scholarly journals Mobile Application for Electric Power Monitoring on Energy Consumptions at a Campus University

2018 ◽  
Vol 11 (2) ◽  
pp. 637
Author(s):  
Murizah Kassim ◽  
Maisarah Abdul Rahman ◽  
Cik Ku Haroswati Che Ku Yahya ◽  
Azlina Idris
Keyword(s):  
Energy Consumption ◽  
Electric Power ◽  
Mobile Applications ◽  
Working Hours ◽  
Mobile App ◽  
Smart Meter ◽  
University Campus ◽  
Smart Meters ◽  
Power Monitoring ◽  
Energy Consumptions

This paper presents a research on electric power monitoring prototype mobile applications development on energy consumptions in a university campus. Electric power energy consumptions always are the issue of monitoring usage especially in a broad environment. University campus faces high used of electric power, thus crucial analysis on cause of the usage is needed. This research aims to analyses electric power usage in a university campus where implemented of few smart meters is installed to monitor five main buildings in a campus university. A Monitoring system is established in collecting electric power usage from the smart meters. Data from the smart meter then is analyzed based on energy consume on 5 buildings. Results presents graph on the power energy consume and presented on mobile applications using Live Code coding. The methodology involved the setup of the smart meters, monitoring and data collected from main smart meters, analyzed electrical consumptions for 5 buildings and mobile system development to monitor. A Live Code mobile app is designed then data collected from smart meter using ION software is published in graphs. Results presents the energy consumed for 5 building during day and night. Details on maximum and minimum energy consumption presented that show load of energy used in the campus. Result present Tower 1 saved most eenergy at night which is 65% compared to block 3 which is 8% saved energy although block 3 presents the lowest energy consumption in the working hours and non-working hours. This project is significant that can help campus facility to monitor electric power used thus able to control possible results in future implementations.

scholarly journals Building energy modeling at neighborhood scale

2020 ◽  
Vol 13 (7) ◽  
pp. 1353-1386 ◽  
Author(s):  
Guglielmina Mutani ◽  
Valeria Todeschi
Keyword(s):  
Energy Consumption ◽  
Energy Balance ◽  
Residential Buildings ◽  
Energy Performance ◽  
Average Error ◽  
Monthly Basis ◽  
Support Tool ◽  
Steady State Method ◽  
Consumption Data ◽  
Neighborhood Scale

Abstract The urban climate and outdoor air quality of cities that have a positive thermal balance depending on the thermal consumptions of buildings cause an increase of the urban heat island and global warming effects. The aim of this work has been to develop an energy balance using the energy consumption data of the district heating network. The here presented engineering energy model is at a neighborhood scale, and the energy-use results have been obtained from a heat balance of residential buildings, by means of a quasi-steady state method, on a monthly basis. The modeling approach also considers the characteristics of the urban context that may have a significant effect on its energy performance. The model includes a number of urban variables, such as solar exposition and thermal radiation lost to the sky of the built environment. This methodology was applied to thirty-three 1 km × 1 km meshes in the city of Turin, using the monthly energy consumption data of three consecutive heating seasons. The results showed that the model is accurate for old built areas; the average error is 10% for buildings constructed before 1970, while the error reaches 20% for newer buildings. The importance and originality of this study are related to the fact that the energy balance is applied at neighborhood scale and urban parameters are introduced with the support of a GIS tool. The resulting engineering models can be applied as a decision support tool for citizens, public administrations, and policy makers to evaluate the distribution of energy consumptions and the relative GHG emissions to promote a more sustainable urban environment. Future researches will be carried out with the aim of introducing other urban variables into the model, such as the canyon effect and the presence of vegetation.

scholarly journals Power analysis of smart home appliances based on SSA-TCN

2021 ◽  
Vol 2078 (1) ◽  
pp. 012060
Author(s):  
Chao Tang ◽  
Yong Tang ◽  
Huihui Liang ◽  
Linghao Zhang ◽  
Siyu Xiang
Keyword(s):  
Energy Consumption ◽  
Power Analysis ◽  
Smart Home ◽  
Singular Spectrum Analysis ◽  
Recognition Rate ◽  
Monitoring Methods ◽  
Convolutional Network ◽  
Home Appliances ◽  
Operation And Maintenance ◽  
Smart Home Appliances

Abstract The popularity of smart home equipment has led to higher requirements for equipment automation operation and maintenance. However, the energy consumption status and hidden faults of household equipment cannot be controlled in time only by using traditional monitoring methods. Therefore, this paper proposes a methods of power analysis for smart home appliances based on SSA-TCN using the energy consumption data of smart home appliances. The effective information of the data is extracted through the SSA singular spectrum analysis method, and the data sequence is input into the sequential convolutional network for judgment, so that the energy consumption status and working status of the equipment is obtained. The actual data is used as the training set and the test set to verify the recognition rate of the model. The experimental results show that the recognition rate of the method is about 82%, which provides an effective way for equipment automation and intelligent operation and maintenance.

scholarly journals Pengembangan Sistem Pemantauan Konsumsi Energi Rumah Tangga Berbasis Internet of Things (IoT)

2018 ◽  
Vol 6 (3) ◽  
pp. 357
Author(s):  
HARTONO BUDI SANTOSO ◽  
SAPTO PRAJOGO ◽  
SRI PARYANTO MURSID
Keyword(s):  
Energy Consumption ◽  
Measurement Accuracy ◽  
Measurement Data ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
Average Error ◽  
Android Application ◽  
Wireless Technology ◽  
Power Meter ◽  
Communication Device

ABSTRAKPenghematan pada konsumsi listrik rumah tangga akan memberikan dampak pada konsumsi listrik nasional. Penelitian menunjukkan pemantauan terhadap konsumsi listrik rumah tangga akan memberikan dampak pada penghematan konsumsi listrik hingga 30%. Beberapa penelitian terkait pengembangan pemantauan terhadap konsumsi listrik rumah tangga masih menunjukkan hasil yang kurang memuaskan. Pada penelitian ini akan dikembangkan sistem pemantauan energi khususnya untuk beban rumah tangga berbasis teknologi IoT, sehingga dapat dilakukan pemantauan menggunaan energi listrik rumah tangga menggunakan aplikasi android di perangkat komunikasi telepon seluler (ponsel). Hasil pengujian akurasi pengukuran, dilakukan dengan membandingkan data pengukuran dengan alat ukur lain, menunjukkan pembacaan arus memiliki rata-rata error sebesar 0% sementara pembacaan tegangan memiliki rata-rata error sebesar 0,06%.Kata kunci: IoT, power meter, power monitor, konsumsi energi ABSTRACTThe savings on household electricity consumption will have an impact on national electricity consumption. Research shows that monitoring of household electricity consumption will have an impact on saving electricity consumption up to 30%. Direct monitoring starts from using cable to wireless technology. Some studies realated to developments of energy consumption monitoring still show unsatisfactory results.In this research will be developed energy monitoring system especially for household load based on IoT technology, so that can be monitored the use of household electrical energy using android application in communication device, handphone. The result of measurement measurement accuracy is done by comparing measurement data with other measuring instrument, indicating current reading has an average error of 0% while the voltage reading has an average error of 0.06%.Keywords: IoT, power meter, power Monitor, energy consumption

Modeling, Validation, and Investigation of an Electrohydraulic Crimping Hand Tool

2019 ◽  
Author(s):  
Eric D. Norquist ◽  
Jonathon E. Slightam ◽  
Mark L. Nagurka
Keyword(s):  
Energy Consumption ◽  
Maximum Energy ◽  
Gear Ratio ◽  
Gear Train ◽  
Lumped Parameter Model ◽  
Average Error ◽  
Hydraulic Systems ◽  
Human Scale ◽  
Hand Tool ◽  
Dependent Parameter

Abstract Due to their high power density, hydraulic systems are increasingly adapted for human scale devices. For example, commercial and utility electricians use electrohydraulic cutting and crimping tools, rather than human powered tools, to cut and crimp wires that exceed 25mm in diameter. These tools greatly reduce worker-related fatigue and strain-type injuries. To improve electrohydraulic tool technology, there is a need to increase the number of applications from a single battery charge. This paper develops a high fidelity nonlinear lumped parameter model of an electrohydraulic crimping hand tool used by professional electricians. The eleventh-order model can predict tool performance with an average error of 6.9% and 4.4% with respect to the maximum energy consumption and crimp time, respectively. Simulation studies were conducted to investigate reducing the energy consumption of the tool. An independent parameter sweep was performed on the pump piston diameter. The gear ratio was a dependent parameter linked through the maximum motor torque. Increasing the pump piston diameter while increasing the gear ratio was shown to decrease the energy consumption of the tool during crimping applications. Simulations suggest that up to 30% energy can be saved per crimp by increasing the pump piston diameter and gear train ratio.

Design of Monitoring Battery Solar Power Plant and Load Control System based Internet of Things

Teknik ◽  
2021 ◽  
Vol 42 (1) ◽  
pp. 35-44
Author(s):  
Riza Alfita ◽  
Koko Joni ◽  
Fajar Dwika Darmawan
Keyword(s):  
Control System ◽  
Power Plant ◽  
Internet Of Things ◽  
Power Consumption ◽  
Solar Power ◽  
Load Control ◽  
Average Error ◽  
Solar Power Plant ◽  
Power Monitoring ◽  
Internet Of Things Technology

Internet of Things technology in this research is utilized on solar power plant (Case Study: Electrical Engineering Department of Trunojoyo Madura University) as a battery power monitoring and load control system. All of these systems were built to make it easier for users to manage the power consumption while preventing battery damage so that lifetime can last longer and the use of PLTS than more optimal. All of these systems are designed to use several integrated components with their respective functions, including Raspberry as a data processing, smartphone as an interface, and sensors actuator as input-output. From the results of the monitoring accuracy test, the average error value is 1.56%. After ensuring the system has a high level of accuracy, The charge-discharge test is conducted in real-time for 7 days, which shows that the system works according to the research objectives as evidenced by the nothingness of power consumption exceeding the SOC standard limit battery used by 30%. Meanwhile, for the control system test, the wifi connection has the fastest average delay for 10,30 s, provider A 11,17 s, and provider B 12,60 s.

scholarly journals AN EFFECT OF A “DEMAND RESPONSE” POWER MODEL TO ENERGY SAVING BEHAVIOR AND ENERGY CONSUMPTION

2015 ◽  
Vol 71 (5) ◽  
pp. I_357-I_368
Author(s):  
Hiroyoshi MORITA ◽  
Chika ISHIDA ◽  
Akio ONISHI ◽  
Shiro KAWAHARA ◽  
Hidefumi IMURA ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Demand Response ◽  
Power Model ◽  
Saving Behavior
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