scholarly journals Two Designs of Automatic Embedded System Energy Consumption Measuring Platforms Using GPIO

2020 ◽  
Vol 10 (14) ◽  
pp. 4866
Author(s):  
Huanjie Wu ◽  
Chun Chen ◽  
Kai Weng
Keyword(s):  
Energy Consumption ◽  
Embedded System ◽  
Critical Evaluation ◽  
General Purpose ◽  
Thermal Design ◽  
Battery Life ◽  
Time Integral ◽  
Consumption Data ◽  
Test Sets ◽  
The Impact

Energy consumption is a critical evaluation index of embedded systems, and it has impacts on battery-life, thermal design, as well as device security and reliability. Since energy is the time integral of power, power consumption should be considered, along with the impact of “time”; thus, we propose two designs of automatic energy consumption measuring platforms utilizing General Purpose Input/Output (GPIO). Using these designs, we developed software and introduced auxiliary hardware for solutions with better timing and synchronization. A series of test sets were designed to verify our designs’ capabilities and accuracy levels. Both of our designs showed an accuracy similar to that of traditional measuring methods, which can satisfy the needs of different occasions. In addition, our designs provide real-time energy consumption data, as well as unattended automated measurements.

scholarly journals Analysis of Office Rooms Energy Consumption Data in Respect to Meteorological and Direct Sun Exposure Conditions

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7590
Author(s):  
Adam Kula ◽  
Albert Smalcerz ◽  
Maciej Sajkowski ◽  
Zygmunt Kamiński
Keyword(s):  
Energy Consumption ◽  
Public Service ◽  
Residential Buildings ◽  
Sun Exposure ◽  
Building Energy ◽  
The West ◽  
Building Energy Management ◽  
Consumption Data ◽  
Office Rooms ◽  
The Impact

There are many papers concerning the consumption of energy in different buildings. Most describe residential buildings, with only a few about office- or public service buildings. Few articles showcase the use of energy consumption in specific rooms of a building, directed in different geographical directions. On the other hand, many publications present methods, such as machine learning or AI, for building energy management and prediction of its consumption. These methods have limitations and represent a certain level of uncertainty. In order to compare energy consumption of different rooms, the measurements of particular building-room parameters were collected and analyzed. The obtained results showcase the effect of room location, regarding geographical directions, for the consumption of energy for heating. For south-exposed rooms, due to sun radiation, it is possible to switch heating off completely, and even overheating of 3 °C above the 22 °C temperature set point occurs. The impact of the sun radiation for rooms with a window directed east or west reached about 1 °C and lasts for a few hours before noon for the east, and until late afternoon for the west.

scholarly journals On the impact of sampling frequency on software energy measurements

2015 ◽  
Author(s):  
Rubén Saborido ◽  
Venera Venera Arnaoudova ◽  
Giovanni Beltrame ◽  
Foutse Khomh ◽  
Giuliano Antoniol
Keyword(s):  
Energy Consumption ◽  
Mobile Applications ◽  
Low Frequency ◽  
Battery Life ◽  
Low Frequencies ◽  
Fine Grain ◽  
Rough Approximation ◽  
Median Error ◽  
Real Picture ◽  
The Impact

Energy consumption is a major concern when developing and evolving mobile applications. The user wishes to access fast and powerful mobile applications, which is usually in contrast to optimized battery life and heat generation. The software engineering community have acknowledged the relevance of the problem and researchers are investigating ways to reduce energy consumption, for example by examining which library, device configuration, and applications parameters should be used to promote long battery life. We conjecture that these studies are at the border between hardware and software and we must be careful on how the energy consumption is measured and how the energy consumption is attributed to methods and libraries.To the best of our knowledge, no previous work investigates how much energy and power consumption is due to high frequency events missed when sampling at low frequencies such as 10 kHz and verified the error at the precision of method level. Low frequency sampling is a rough approximation that hinders the understanding of fine grain details: the real picture of energy consumption as well as the root causes are missed. This has profound implications on the choice of methods to evolve or components to replace.In this paper, we propose an approach for accurate measurements of the energy consumption of mobile applications. We apply the proposed approach to assess the energy consumption of 21 mobile, closed source, applications and four open source Android applications.We show that by sampling at 10 kHz one may expect a median error of 8%, however, such error may be as high as 50% for short fast executing methods. Finally, we revisit a previous approach that estimates the energy consumption of methods based on execution time and found that it can miss as much as 84% of the energy, with a median of 30%.

scholarly journals On the impact of sampling frequency on software energy measurements

2015 ◽  
Author(s):  
Rubén Saborido ◽  
Venera Venera Arnaoudova ◽  
Giovanni Beltrame ◽  
Foutse Khomh ◽  
Giuliano Antoniol
Keyword(s):  
Energy Consumption ◽  
Mobile Applications ◽  
Low Frequency ◽  
Battery Life ◽  
Low Frequencies ◽  
Fine Grain ◽  
Rough Approximation ◽  
Median Error ◽  
Real Picture ◽  
The Impact

Energy consumption is a major concern when developing and evolving mobile applications. The user wishes to access fast and powerful mobile applications, which is usually in contrast to optimized battery life and heat generation. The software engineering community have acknowledged the relevance of the problem and researchers are investigating ways to reduce energy consumption, for example by examining which library, device configuration, and applications parameters should be used to promote long battery life. We conjecture that these studies are at the border between hardware and software and we must be careful on how the energy consumption is measured and how the energy consumption is attributed to methods and libraries.To the best of our knowledge, no previous work investigates how much energy and power consumption is due to high frequency events missed when sampling at low frequencies such as 10 kHz and verified the error at the precision of method level. Low frequency sampling is a rough approximation that hinders the understanding of fine grain details: the real picture of energy consumption as well as the root causes are missed. This has profound implications on the choice of methods to evolve or components to replace.In this paper, we propose an approach for accurate measurements of the energy consumption of mobile applications. We apply the proposed approach to assess the energy consumption of 21 mobile, closed source, applications and four open source Android applications.We show that by sampling at 10 kHz one may expect a median error of 8%, however, such error may be as high as 50% for short fast executing methods. Finally, we revisit a previous approach that estimates the energy consumption of methods based on execution time and found that it can miss as much as 84% of the energy, with a median of 30%.

The Impact of Data Granularity of Indoor Temperature Measurements on the Calculation of Degree Days

2021 ◽  
Vol 25 (1) ◽  
pp. 1305-1316
Author(s):  
Xue Mei ◽  
Carlos Jimenez-Bescos
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
The United States ◽  
Base Temperature ◽  
Degree Days ◽  
Indoor Temperature ◽  
Consumption Data ◽  
Data Points ◽  
Data Granularity ◽  
The Impact

Abstract Degree-days are to normalise energy consumption data and furthermore can generate forecasting predictions for energy demand being used to compare between different properties across different location and years. The base temperature is the main factor to consider the accuracy of degree days. The aim of this study was to evaluate the impact of data granularity to understand its effect on a correlation between energy consumption and Degree Days. Degree Days were calculated using the standard 18.3 °C base temperature as taking in the United States of America and compare the Degree Days calculations against the calculation based on hourly, daily and monthly data for base temperature. The methodology followed is based on the analysis of 23 houses located in Texas, Austin. The properties under study are from different construction periods and with a variety of total floor areas. This study had demonstrated the effect of the granularity of the data collected to generate Degree Days and its impact on the correlation between energy consumption and degree-days for different base temperatures. While the higher correlations are achieved using a monthly granularity, this approach is not recommended due to the small number of data points and a much more preferred approach that should be taken is a daily approach, which would generate a much more reliable correlation. In this study, higher correlation values were achieved when using the standard 18.3 °C base temperature for the Degree Days calculations, 70 % correlation in daily approach versus 56.67 % using indoor temperature, showing better results across the board against the use of indoor temperature at all granularity levels.

Impact of roof shading on building energy performance in warm & humid climatic places of India

2020 ◽  
pp. 50-64
Author(s):  
Kuladeep Kumar Sadevi ◽  
Avlokita Agrawal
Keyword(s):  
Energy Consumption ◽  
Energy Conservation ◽  
Energy Performance ◽  
General Assumption ◽  
Climatic Conditions ◽  
Building Envelope ◽  
Passive Cooling ◽  
Base Case ◽  
U Value ◽  
The Impact

With the rise in awareness of energy efficient buildings and adoption of mandatory energy conservation codes across the globe, significant change is being observed in the way the buildings are designed. With the launch of Energy Conservation Building Code (ECBC) in India, climate responsive designs and passive cooling techniques are being explored increasingly in building designs. Of all the building envelope components, roof surface has been identified as the most significant with respect to the heat gain due to the incident solar radiation on buildings, especially in tropical climatic conditions. Since ECBC specifies stringent U-Values for roof assembly, use of insulating materials is becoming popular. Along with insulation, the shading of the roof is also observed to be an important strategy for improving thermal performance of the building, especially in Warm and humid climatic conditions. This study intends to assess the impact of roof shading on building’s energy performance in comparison to that of exposed roof with insulation. A typical office building with specific geometry and schedules has been identified as base case model for this study. This building is simulated using energy modelling software ‘Design Builder’ with base case parameters as prescribed in ECBC. Further, the same building has been simulated parametrically adjusting the amount of roof insulation and roof shading simultaneously. The overall energy consumption and the envelope performance of the top floor are extracted for analysis. The results indicate that the roof shading is an effective passive cooling strategy for both naturally ventilated and air conditioned buildings in Warm and humid climates of India. It is also observed that a fully shaded roof outperforms the insulated roof as per ECBC prescription. Provision of shading over roof reduces the annual energy consumption of building in case of both insulated and uninsulated roofs. However, the impact is higher for uninsulated roofs (U-Value of 3.933 W/m2K), being 4.18% as compared to 0.59% for insulated roofs (U-Value of 0.33 W/m2K).While the general assumption is that roof insulation helps in reducing the energy consumption in tropical buildings, it is observed to be the other way when insulation is provided with roof shading. It is due to restricted heat loss during night.

ANALISIS DETERMINAN KUALITAS LINGKUNGAN PERIODE 1999-2013

2016 ◽  
Vol 21 (1) ◽  
pp. 9-20
Author(s):  
Ersalina Tang
Keyword(s):  
Foreign Direct Investment ◽  
Energy Consumption ◽  
Gross Domestic Product ◽  
Co2 Emissions ◽  
Direct Investment ◽  
Meat Consumption ◽  
Domestic Product ◽  
World Data ◽  
The World ◽  
The Impact

The purpose of this study is to analyze the impact of Foreign Direct Investment, Gross Domestic Product, Energy Consumption, Electric Consumption, and Meat Consumption on CO2 emissions of 41 countries in the world using panel data from 1999 to 2013. After analyzing 41 countries in the world data, furthermore 17 countries in Asia was analyzed with the same period. This study utilized quantitative approach with Ordinary Least Square (OLS) regression method. The results of 41 countries in the world data indicates that Foreign Direct Investment, Gross Domestic Product, Energy Consumption, and Meat Consumption significantlyaffect Environmental Qualities which measured by CO2 emissions. Whilst the results of 17 countries in Asia data implies that Foreign Direct Investment, Energy Consumption, and Electric Consumption significantlyaffect Environmental Qualities. However, Gross Domestic Product and Meat Consumption does not affect Environmental Qualities.

Compiler-Aided Run-Time Performance Speed-Up in Super-Scalar Processor

10.28945/3391 ◽  
2009 ◽  
Author(s):  
Moshe Pelleh
Keyword(s):  
Embedded Systems ◽  
Embedded System ◽  
General Purpose ◽  
Experimental Results ◽  
Medium Size ◽  
Organic System ◽  
Software Application ◽  
Organic Systems ◽  
Special Cases ◽  
Run Time

In our world, where most systems become embedded systems, the approach of designing embedded systems is still frequently similar to the approach of designing organic systems (or not embedded systems). An organic system, like a personal computer or a work station, must be able to run any task submitted to it at any time (with certain constrains depending on the machine). Consequently, it must have a sophisticated general purpose Operating System (OS) to schedule, dispatch, maintain and monitor the tasks and assist them in special cases (particularly communication and synchronization between them and with external devices). These OSs require an overhead on the memory, on the cache and on the run time. Moreover, generally they are task oriented rather than machine oriented; therefore the processor's throughput is penalized. On the other hand, an embedded system, like an Anti-lock Braking System (ABS), executes always the same software application. Frequently it is a small or medium size system, or made up of several such systems. Many small or medium size embedded systems, with limited number of tasks, can be scheduled by our proposed hardware architecture, based on the Motorola 500MHz MPC7410 processor, enhancing its throughput and avoiding the software OS overhead, complexity, maintenance and price. Encouraged by our experimental results, we shall develop a compiler to assist our method. In the meantime we will present here our proposal and the experimental results.

The Nexus between Financial Development, Economic Growth and Energy Consumption in India: A VECM Technique

2020 ◽  
pp. 602-612
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Financial Development ◽  
Consumer Price Index ◽  
Development Economic ◽  
Energy Prices ◽  
Vector Error Correction ◽  
Percent Increase ◽  
Growth Energy ◽  
The Impact

The demand for energy consumption requires efficient financial development in terms of bank credit. Therefore, this study examines the nexus between Financial Development, Economic Growth, Energy Prices and Energy Consumption in India, utilizing Vector Error Correction Model (VECM) technique to determine the nature of short and long term relationships from 2010 to 2019. The estimation of results indicates that a one percent increase in bank credits to private sector results in 0.10 percent increase in energy consumption and 0.28 percent increase in energy consumption responses to 1 percent increase in economic growth. It is also observed that the impact of energy price proxied by consumer price index is statistically significant with a negative sign indicating the consistency with the theory.

Design and Implementation of Low Energy Wireless Network Nodes based on Hardware Compression Acceleration

2019 ◽  
Vol 12 ◽  
Author(s):  
Hui Yang ◽  
Anand Nayyar
Keyword(s):  
Energy Consumption ◽  
Data Compression ◽  
Energy Saving ◽  
Optimization Design ◽  
Hardware Acceleration ◽  
Transmission Efficiency ◽  
General Purpose ◽  
Storage Space ◽  
General Purpose Processor ◽  
Compression Time

: In the fast development of information, the information data is increasing in geometric multiples, and the speed of information transmission and storage space are required to be higher. In order to reduce the use of storage space and further improve the transmission efficiency of data, data need to be compressed. processing. In the process of data compression, it is very important to ensure the lossless nature of data, and lossless data compression algorithms appear. The gradual optimization design of the algorithm can often achieve the energy-saving optimization of data compression. Similarly, The effect of energy saving can also be obtained by improving the hardware structure of node. In this paper, a new structure is designed for sensor node, which adopts hardware acceleration, and the data compression module is separated from the node microprocessor.On the basis of the ASIC design of the algorithm, by introducing hardware acceleration, the energy consumption of the compressed data was successfully reduced, and the proportion of energy consumption and compression time saved by the general-purpose processor was as high as 98.4 % and 95.8 %, respectively. It greatly reduces the compression time and energy consumption.

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