scholarly journals Did I make a mistake? Finding the impact of code change on energy regression

2017 ◽  
Author(s):  
Shaiful Alam Chowdhury ◽  
Stephanie Gil ◽  
Stephen Romansky ◽  
Abram Hindle
Keyword(s):  
Energy Consumption ◽  
Model Building ◽  
Continuous Process ◽  
Measurement Data ◽  
Energy Measurement ◽  
Test Cases ◽  
End User ◽  
Energy Models ◽  
Software Energy Consumption ◽  
The Impact

Software energy consumption is a performance related non-functional requirement that complicates building software on mobile devices today. Energy hogging applications are a liability to both the end-user and software developer. Measuring software energy consumption is non-trivial, requiring both equipment and expertise, yet many researchers have found that software energy consumption can be modelled. Prior works have hinted that with more energy measurement data one can make more accurate energy models but this data was expensive to extract because it required energy measurement of running test cases (rare) or time consuming manually written tests. We address these concerns by automatically generating test cases to drive applications undergoing energy measurement. Automatic test generation allows a model to be continuously improved in a model building process whereby applications are extracted, tests are generated, energy is measured and combined with instrumentation to train a grander big-data model of software energy consumption. This continuous process has allowed the authors to generate and extract measurements from hundreds of applications in order to build accurate energy models capable of predicting the energy consumption of applications without end-user energy measurement. We clearly show that models built from more applications reduce energy modelling error.

scholarly journals Did I make a mistake? Finding the impact of code change on energy regression

2017 ◽  
Author(s):  
Shaiful Alam Chowdhury ◽  
Stephanie Gil ◽  
Stephen Romansky ◽  
Abram Hindle
Keyword(s):  
Energy Consumption ◽  
Model Building ◽  
Continuous Process ◽  
Measurement Data ◽  
Energy Measurement ◽  
Test Cases ◽  
End User ◽  
Energy Models ◽  
Software Energy Consumption ◽  
The Impact

Software energy consumption is a performance related non-functional requirement that complicates building software on mobile devices today. Energy hogging applications are a liability to both the end-user and software developer. Measuring software energy consumption is non-trivial, requiring both equipment and expertise, yet many researchers have found that software energy consumption can be modelled. Prior works have hinted that with more energy measurement data one can make more accurate energy models but this data was expensive to extract because it required energy measurement of running test cases (rare) or time consuming manually written tests. We address these concerns by automatically generating test cases to drive applications undergoing energy measurement. Automatic test generation allows a model to be continuously improved in a model building process whereby applications are extracted, tests are generated, energy is measured and combined with instrumentation to train a grander big-data model of software energy consumption. This continuous process has allowed the authors to generate and extract measurements from hundreds of applications in order to build accurate energy models capable of predicting the energy consumption of applications without end-user energy measurement. We clearly show that models built from more applications reduce energy modelling error.

scholarly journals GreenScaler: Automatically training software energy model with big data

2016 ◽  
Author(s):  
Shaiful Alam Chowdhury ◽  
Stephanie Gil ◽  
Stephen Romansky ◽  
Abram Hindle
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Model Building ◽  
Continuous Process ◽  
Measurement Data ◽  
Energy Measurement ◽  
Test Cases ◽  
End User ◽  
Energy Models ◽  
Software Energy Consumption

Software energy consumption is a performance related non-functional requirement that complicates building software on mobile devices today. Energy hogging applications are a liability to both the end-user and software developer. Measuring software energy consumption is non-trivial, requiring both equipment and expertise, yet many researchers have found that software energy consumption can be modelled. Prior works have hinted that with more energy measurement data one can make more accurate energy models but this data was expensive to extract because it required energy measurement of running test cases (rare) or time consuming manually written tests. We address these concerns by automatically generating test cases to drive applications undergoing energy measurement. Automatic test generation allows a model to be continuously improved in a model building process whereby applications are extracted, tests are generated, energy is measured and combined with instrumentation to train a grander big-data model of software energy consumption. This continuous process has allowed the authors to generate and extract measurements from hundreds of applications in order to build accurate energy models capable of predicting the energy consumption of applications without end-user energy measurement. We clearly show that models built from more applications reduce energy modelling error.

scholarly journals GreenScaler: Automatically training software energy models with big data

2016 ◽  
Author(s):  
Shaiful Alam Chowdhury ◽  
Stephanie Gil ◽  
Stephen Romansky ◽  
Abram Hindle
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Model Building ◽  
Continuous Process ◽  
Measurement Data ◽  
Energy Measurement ◽  
Test Cases ◽  
End User ◽  
Energy Models ◽  
Software Energy Consumption

Software energy consumption is a performance related non-functional requirement that complicates building software on mobile devices today. Energy hogging applications are a liability to both the end-user and software developer. Measuring software energy consumption is non-trivial, requiring both equipment and expertise, yet many researchers have found that software energy consumption can be modelled. Prior works have hinted that with more energy measurement data one can make more accurate energy models but this data was expensive to extract because it required energy measurement of running test cases (rare) or time consuming manually written tests. We address these concerns by automatically generating test cases to drive applications undergoing energy measurement. Automatic test generation allows a model to be continuously improved in a model building process whereby applications are extracted, tests are generated, energy is measured and combined with instrumentation to train a grander big-data model of software energy consumption. This continuous process has allowed the authors to generate and extract measurements from hundreds of applications in order to build accurate energy models capable of predicting the energy consumption of applications without end-user energy measurement. We clearly show that models built from more applications reduce energy modelling error.

scholarly journals The influence of design features of housing facilities on energy consumption

2020 ◽  
Vol 175 ◽  
pp. 11019
Author(s):  
Sergei Kolodyazhniy ◽  
Valeriy Mishchenko ◽  
Elena Gorbaneva ◽  
Kristina Sevryukova
Keyword(s):  
Energy Consumption ◽  
Residential Buildings ◽  
Structural Characteristics ◽  
Electricity Consumption ◽  
End User ◽  
Total Energy Consumption ◽  
Apartment Buildings ◽  
Energy Models ◽  
Actual Energy Consumption ◽  
The Impact

This article analyzed the impact of the structural characteristics of old apartment buildings on actual energy consumption. The authors reviewed energy consumption in existing apartment buildings in Voronezh in order to determine the need for major repairs and energy efficiency. For this purpose, a comparative analysis of energy consumption in old apartment buildings and in new ones built in accordance with the current regulations was carried out. Three indicators of energy consumption were considered for analysis: total energy consumption by the end-user, heating of premises and electricity consumption depending on the year of construction of apartment buildings. The characteristics considered were used to quantify energy consumption (heating and power supply). Due to the results obtained, a statistical analysis of energy consumption in old apartment buildings and in new ones was carried out. It was noted that old apartment buildings consume more energy than those built at a late stage, in accordance with the current regulatory framework. The results can be useful in identifying priority elements of the building that will help to effectively reduce energy consumption during major repairs and classify existing residential buildings to build energy models.

The Impact of COVID-19 on Energy Consumption in the United States: An Overview

2021 ◽  
Author(s):  
Lindsey Kahn ◽  
Hamidreza Najafi
Keyword(s):  
Growth Rate ◽  
Energy Consumption ◽  
Energy Use ◽  
The United States ◽  
Average Annual Growth Rate ◽  
Annual Growth ◽  
Annual Growth Rate ◽  
End User ◽  
End Use ◽  
The Impact

Abstract Lockdown measures and mobility restrictions implemented to combat the spread of the novel COVID-19 virus have impacted energy consumption patterns, particularly in the United States. A review of available data and literature on the impact of the pandemic on energy consumption is performed to understand the current knowledge on this topic. The overall decline of energy use during lockdown restrictions can best be identified through the analysis of energy consumption by source and end-user breakdown. Using monthly energy consumption data, the total 9-months use between January and September for the years 2015–2020 are calculated for each end-use. The cumulative consumption within these 9 months of the petroleum, natural gas, biomass, and electricity energy by the various end-use sectors are compared to identify a shift in use throughout time with the calculation of the percent change from 2019 to 2020. The analysis shows that the transportation sector experienced the most dramatic decline, having a subsequent impact on the primary energy it uses. A steep decline in the use of petroleum and natural gas by the transportation sector has had an inevitable impact on the emission of carbon dioxide and other air pollutants during the pandemic. Additionally, the most current data for the consumption of electricity by each state and each end-user in the times before and during the pandemic highlights the impact of specific lockdown procedures on energy use. The average total consumption for each state was found for the years 2015–2019. This result is used calculation of yearly growth rate and average annual growth rate in 2020 for each state and end-user. The total average annual growth rate for 2020 was used to find a correlation coefficient between COVID-19 case and death rates as well as population density and lockdown duration. To further examine the relationship a correlation coefficient was calculated between the 2020 average annual growth rate for all sectors and average annual growth rate for each individual end-user.

scholarly journals An evaluation of the effects of thermal insulation levels on the energy performance of New Zealand office buildings- exploring the impact of building attributes on the performance of thermal insulation

2021 ◽  
Author(s):  
◽  
Brittany Grieve
Keyword(s):  
Energy Consumption ◽  
New Zealand ◽  
Thermal Insulation ◽  
Building Energy ◽  
Energy Performance ◽  
Building Envelope ◽  
Office Buildings ◽  
Building Performance ◽  
Energy Models ◽  
The Impact

<p>This thesis explored the impact of thermal insulation on the energy performance of New Zealand air-conditioned commercial office buildings. A sample of calibrated energy models constructed using real building performance data and construction information was used to ensure that the results produced were as realistic as possible to the actual building performance of New Zealand commercial office buildings. The aim was to assess how different climates and building attributes impact thermal insulation's ability to reduce energy consumption in New Zealand commercial office buildings.   Driven by the ever increasing demands for healthier, more comfortable, more sustainable buildings, building regulations have steadily increased the levels of insulation they require in new buildings over time. Improving the thermal properties of the building envelope with the addition of thermal insulation is normally used to reduce the amount of heating and cooling energy a building requires. Thermal insulation reduces the conductive heat transfer through the building envelope and with a higher level of thermal resistance, the less heat would transfer through the envelope. Consequently, the common expectation is that the addition of thermal insulation to the building envelope will always reduce energy consumption. However, this assumption is not always the case. For internal load dominated buildings located in certain climates, the presence of any or a higher level of thermal insulation may prevent heat loss through the wall, increasing the cooling energy required. This issue is thought to have not been directly examined in literature until 2008. However, an early study undertaken in New Zealand in 1996 found that for climates similar or warmer than Auckland, the addition of insulation could be detrimental to an office building's energy efficiency due to increased cooling energy requirements.  The energy performance of a sample of 13 real New Zealand office building energy models with varying levels of thermal insulation in 8 locations was examined under various scenarios. A parametric method of analysis using building energy modelling was used to assess the energy performance of the buildings. Buildings were modelled as built and standardised with the current NZS4243:2007 regulated and assumed internal load and operational values. The effect the cooling thermostat set point temperature had on the buildings' energy performance at varying levels of insulation was also tested.   The study concluded that the use of thermal insulation in New Zealand office buildings can cause an increase in cooling energy for certain types of buildings in any of the eight locations and thermal insulation levels explored in the study. The increase in cooling energy was significant enough to increase the total energy consumption of two buildings when modelled as built. These buildings were characterised by large internal loads, low performance windows with high window to wall ratios and low surface to volume ratios. The current minimum thermal resistance requirements were found to not be effective for a number of buildings in North Island locations.</p>

scholarly journals An evaluation of the effects of thermal insulation levels on the energy performance of New Zealand office buildings- exploring the impact of building attributes on the performance of thermal insulation

2021 ◽  
Author(s):  
◽  
Brittany Grieve
Keyword(s):  
Energy Consumption ◽  
New Zealand ◽  
Thermal Insulation ◽  
Building Energy ◽  
Energy Performance ◽  
Building Envelope ◽  
Office Buildings ◽  
Building Performance ◽  
Energy Models ◽  
The Impact

<p>This thesis explored the impact of thermal insulation on the energy performance of New Zealand air-conditioned commercial office buildings. A sample of calibrated energy models constructed using real building performance data and construction information was used to ensure that the results produced were as realistic as possible to the actual building performance of New Zealand commercial office buildings. The aim was to assess how different climates and building attributes impact thermal insulation's ability to reduce energy consumption in New Zealand commercial office buildings.   Driven by the ever increasing demands for healthier, more comfortable, more sustainable buildings, building regulations have steadily increased the levels of insulation they require in new buildings over time. Improving the thermal properties of the building envelope with the addition of thermal insulation is normally used to reduce the amount of heating and cooling energy a building requires. Thermal insulation reduces the conductive heat transfer through the building envelope and with a higher level of thermal resistance, the less heat would transfer through the envelope. Consequently, the common expectation is that the addition of thermal insulation to the building envelope will always reduce energy consumption. However, this assumption is not always the case. For internal load dominated buildings located in certain climates, the presence of any or a higher level of thermal insulation may prevent heat loss through the wall, increasing the cooling energy required. This issue is thought to have not been directly examined in literature until 2008. However, an early study undertaken in New Zealand in 1996 found that for climates similar or warmer than Auckland, the addition of insulation could be detrimental to an office building's energy efficiency due to increased cooling energy requirements.  The energy performance of a sample of 13 real New Zealand office building energy models with varying levels of thermal insulation in 8 locations was examined under various scenarios. A parametric method of analysis using building energy modelling was used to assess the energy performance of the buildings. Buildings were modelled as built and standardised with the current NZS4243:2007 regulated and assumed internal load and operational values. The effect the cooling thermostat set point temperature had on the buildings' energy performance at varying levels of insulation was also tested.   The study concluded that the use of thermal insulation in New Zealand office buildings can cause an increase in cooling energy for certain types of buildings in any of the eight locations and thermal insulation levels explored in the study. The increase in cooling energy was significant enough to increase the total energy consumption of two buildings when modelled as built. These buildings were characterised by large internal loads, low performance windows with high window to wall ratios and low surface to volume ratios. The current minimum thermal resistance requirements were found to not be effective for a number of buildings in North Island locations.</p>

Effect of Driver and Work Cycle on Losses of a Loader

2018 ◽  
Author(s):  
Seppo Tikkanen ◽  
Ville Ahola ◽  
Elias Koskela
Keyword(s):  
Energy Consumption ◽  
Energy Distribution ◽  
Fuel Consumption ◽  
Measurement Data ◽  
Initial State ◽  
Design Work ◽  
Skill Levels ◽  
Work Cycle ◽  
The Difference ◽  
The Impact

Improving the energy efficiency of mobile machines requires information about the initial state of the machine. This information includes knowledge of the systems and their components and of course, measurement data that is acquired during typical operation. Machine manufacturers and research institutes have carried out extensive measurement programs during the last decade. Usually, the published studies concentrated on one work cycle, the machines studied were operated by humans, and it is shown that productivity and fuel consumption are dependent on the machine design, work cycle and operator. This study concentrates on a detailed analysis of the energy consumption of a municipal loader during measured work tasks. The goal was to find out how much the driver and work cycle affect the machine’s energy consumption and energy distribution. To evaluate the real fuel consumption and energy distribution, the measurements consisted of two different work cycles that were driven by two drivers with different skill levels. The first cycle was the classic short wheel loader loading cycle, the Y-cycle. In this task, the loader was equipped with a bucket, and a pile of gravel was moved from pile A to pile B in a Y-pattern. The second cycle was the load and carry cycle in which the driver picked up a load with the forklift attachment and carried the load over a predefined distance. The major finding was that the impact of the driver and the work cycle is considerable in fuel consumption. The difference is also seen in the energy distribution in the hydraulic system and in losses and how the losses are divided. Therefore, it can be stated that test results with one driver or one cycle should not be generalized without concern and judgement of novel concepts requires several tests with different drivers and work cycles.

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.

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