Graph-based algorithms for comparison and prediction of household-level energy use profiles

Author(s):  
Nathaniel Charlton ◽  
Danica Vukadinovic Greetham ◽  
Colin Singleton
2021 ◽  
Author(s):  
Marta Baltruszewicz ◽  
Julia Steinberger ◽  
Diana Ivanova ◽  
Lina Brand-Correa ◽  
Jouni Paavola ◽  
...  

<p>The link between energy use, social and environmental well-being is at the root of critical synergies between clean and affordable energy (SDG7) and other SDGs. Household-level quantitative energy analyses enable better understanding regarding interconnections between the level and composition of energy use, and SDG achievement. This study examines the household-level energy footprints in Nepal, Vietnam, and Zambia. We calculate the footprints using multi-regional input-output (MRIO) with energy extensions based on International Energy Agency (IEA) data. We propose an original perspective on the links between household final energy use and well-being, measured through access to safe water, health, education, sustenance, and modern fuels. In all three countries, households with high well-being show much lower housing energy use, due to a transition from inefficient<br>biomass-based traditional fuels to efficient modern fuels, such as gas and electricity. We find that households achieving wellbeing have 60-80% lower energy footprint of residential fuel use compared to average across the countries. We observe that collective provisioning systems in form of access to health centres, public transport, markets, and garbage disposal and characteristics linked to having solid shelter, access to sanitation, and minimum floor area are more important for the attainment of wellbeing than changes in income or total energy consumption. This is an important finding,  contradicting the narrative that basic wellbeing outcomes require increased income and individual consumption of energy. Substantial synergies exist between the achievement of well-being at a low level of energy use and other SDGs linked to poverty reduction (encompassed in SDG1), health (SDG3), sanitation (SDG6), gender equality (SDG5), climate action and reduced deforestation (SDG 13 and SDG15) and inequalities (SDG10). </p>


IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 33498-33511
Author(s):  
Dabeeruddin Syed ◽  
Haitham Abu-Rub ◽  
Ali Ghrayeb ◽  
Shady S. Refaat

2019 ◽  
Vol 53 ◽  
pp. 59-67 ◽  
Author(s):  
Marie Jürisoo ◽  
Nancy Serenje ◽  
Francis Mwila ◽  
Fiona Lambe ◽  
Matthew Osborne

1998 ◽  
Vol 15 (2) ◽  
pp. 171-180 ◽  
Author(s):  
M.N. Bari ◽  
D.O. Hall ◽  
N.J.D. Lucas ◽  
S.M.A. Hossain

2014 ◽  
Vol 6 (4) ◽  
pp. 207-238 ◽  
Author(s):  
Lucas W. Davis ◽  
Alan Fuchs ◽  
Paul Gertler

This paper evaluates a large-scale appliance replacement program in Mexico that from 2009 to 2012 helped 1.9 million households replace their old refrigerators and air conditioners with energy-efficient models. Using household-level billing records from  the universe of Mexican residential customers, we find that refrigerator replacement reduces electricity consumption by 8 percent, about one-quarter of what was predicted by ex ante analyses. Moreover, we find that air conditioning replacement actually increases electricity consumption. Overall, we find that the program is an expensive way to reduce externalities from energy use, reducing carbon dioxide emissions at a program cost of over $500 per ton. (JEL L68, L94, O12, O13, Q41, Q54)


2021 ◽  
Author(s):  
Shuyang Chen

Abstract Despite the significant impacts of technology on the socioeconomic effects of climate policies, many previous researchers neglected the induced technical impacts and thus resulted in biased evaluations of climate policies. Hence, it is important that the induced technology should be endogenized in the policy evaluation framework. In this paper, I attempt to use a Computable General Equilibrium (CGE) model to quantify the technical impacts of the Chinese carbon tax. The technical impacts are denoted by the induced technological change (ITC), which is a function of the energy-use efficiency (EUE), energy-production efficiency (EPE), and nonenergy-production efficiency (ENE). The carbon tax will increase the energy cost share because the of the internalisation of the abatement costs. This paper empirically shows that the carbon tax will decrease the energy cost share and production efficiency but increase the energy use and nonenergy production efficiency. Overall, the carbon tax will promote the technological development, compared to the baseline scenario. In addition to the policy effects of the tax, the ITC will decrease the energy use and production efficiency but increase the nonenergy production efficiency. The ITC will increase the RGDP, decrease the household welfare, and increase the average social cost of carbon (ASCC). To summarise, despite that the carbon tax will decrease the welfare at the country and household level, the ITC of the carbon tax will increase the welfare at the country level but decrease the welfare at the household level. Under the ITC impacts, the emission abatement will become costlier.


2021 ◽  
Author(s):  
Craig Brown

The quest to ‘green’ the built environment has been ongoing since the early 1970s and has intensified as the threat of exceeding 450 ppm of atmospheric carbon dioxide has become more real. As a result of this, many contemporary residential high-rise buildings are designed with hopes of achieving carbon emission reductions, while not sacrificing occupant satisfaction, or property value. Little is known about how the occupants of these buildings contribute to the energy and water consumed therein, nor the effects that these design aspirations have on occupant satisfaction. The present study relies on data collected in four recently built, Leadership in Energy and Environmental Design [LEED] certified, high-rise, residential buildings in Ontario, Canada. Using various sources of data (i.e., from energy and water submeters, questionnaire responses, interviews, and physical data relating to each suite) the extent to which physical, behavioural, and demographic variables explain suite-level energy and water consumption was explored. Energy use intensity differed by a factor of 7 between similar suites, electricity by a factor of 5, hot water by a factor of 13, cooling by a factor of 47, and heating by a factor of 67. Results show that physical building characteristics explain 43% of the heating variability, 16% of the cooling variability, and 40% of electricity variability, suggesting that the remainders could be a result of occupant behaviour and demographics. It was also discovered that 52% of respondents were not using their energy recovery ventilators [ERV] for the following reasons: acoustic dissatisfaction, difficulty with accessibility of filters, occupant knowledge and preferences, and a lack of engagement with training materials. Results suggest that abandoning mechanical ventilation in favour of passive ventilation could actually lead to greater satisfaction with indoor air quality and to decreased energy consumption. Using content analysis of questionnaire comments, the utility of contextual factors in understanding energy use and satisfaction in the study buildings, as well as their value in producing feedback for designers and managers, was explored. Combining quantitative and qualitative datasets was an effective approach to understanding energy use in this understudied building type.


2019 ◽  
Vol 15 (2) ◽  
pp. 73-82
Author(s):  
Alok Pandey ◽  
Annapurna Dixit

In the present study an attempt has been made to estimate the responsiveness of prices and household expenditure on consumption of energy for cooking and lighting at household level in rural and urban areas of All India. Household level energy elasticities are estimated for the rural and urban areas with the help of dummy variable regression approach by using NSSO 66th quinnquenial rounds of unit level data.. The results reveal the fact that average expenditure recorded on energy for cooking and lighting in urban areas is higher than in rural areas at all India level. Majority of the households are using dirty fuel for cooking in rural areas while in urban areas clean fuel i.e. LPG is used for cooking. The expenditure on energy for cooking and lighting at household level is inelastic. The marginal budget share in rural and urban areas is the same. Result reveal the fact that hundred percent increase in prices of energy for cooking and lighting will increase the expenditure of households in rural region more than in urban region.


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