Estimating country-specific space heating threshold temperatures from national gas and electricity consumption data

Kozarcanin, Smail; Andresen, Gorm Bruun; Staffell, Iain

doi:10.1016/j.enbuild.2019.07.013

Cited by 19 publications

(15 citation statements)

References 36 publications

(40 reference statements)

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“…Across regions, heating thresholds range from 9.7 to 15.2°C, which is similar to the average thresholds derived for Europe in [72] (i.e. 13.4 and 15.0°C for electric and gas heating respectively).…”

Section: Calibrating the Modelsupporting

confidence: 78%

Satellite weather data accurately models global space heating and cooling demand

Staffell

Pfenninger

Johnson

2022

Preprint

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Various tools exist for modelling the temporal and spatial impacts of weather on energy supply, yet few consider these impacts on energy demand for heating and cooling in the same universal way. Available models are limited in their applicability or validate poorly against measured energy consumption. Here, we present a model of hourly heating and cooling demand that can be applied globally at all scales and customised to specific contexts. We validate it against energy consumption from ~5,000 individual buildings and 43 nations / regions spanning four continents. We then assess two contemporary case studies. Firstly, we show that ‘turning down the thermostat’ by 1°C across all buildings would reduce Europe’s reliance on Russian natural gas imports by one-fifth, and allow the US to increase gas exports by one-tenth. Secondly, we show that providing a ‘comfortable’ standard of space cooling globally would require nearly half of current global electricity demand. The model and underlying data are available freely on an interactive platform to aid further research in this area.

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Section: Calibrating the Modelsupporting

confidence: 78%

Satellite weather data accurately models global space heating and cooling demand

Staffell

Pfenninger

Johnson

2022

Preprint

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“…As buildings are a large thermal energy storage, and many buildings have a boiler, heat pumps can be operated a few hours ahead of the heat demand, e.g., in the night hours when wind power is often available and the grid load is low. In spring and autumn 47 , coupling heat pumps with local PV can provide heat during the day. This flexibility is not incorporated in the load curve in Supplementary Fig.…”

Section: Methodsmentioning

confidence: 99%

Replacing gas boilers with heat pumps is the fastest way to cut German gas consumption

Altermatt¹,

Clausen

Brendel

et al. 2023

Commun Earth Environ

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The supply security of fossil gas has been disrupted by the Russo-Ukrainian War. Decisions to relocate the production and transport of gas have become so urgent that new long-term contracts are imminent that undermine the Paris Climate Agreement. Here, we simulate how quickly the addition of renewable electricity and the installation of heat pumps can substitute enough gas to reduce supply risk, while taking a decisive step towards meeting the Paris Agreement. Our bottom-up modelling, using Germany as an example, shows technical pathways on how installing heat pumps is one of the fastest ways to reduce gas consumption, in addition to reducing the load hours of gas-fired power plants. With targeted efforts, maximally 60% of gas from the Russian Federation can be substituted by 2025 with heat pumps and grid expansions, and enough electricity will remain available that the phase-out of coal and the entry into e-mobility will still be practicable.

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“…As described in Figure 4, the contribution of space heating to the difference in total energy consumption is more than half, reaching 56.29%, firstly because space heating has the highest Gini coefficient and secondly owing to the proportion of space heating in the total consumption reaching 44.84% [53]. The Gini coefficient for space cooling is higher than that for household appliance power supply at 0.61 but contributes 22.18% to the difference in total energy consumption due to its much higher consumption than cooking, household appliance power supply, and plumbing.…”

Section: Analysis Based On Decomposing Gini Coefficientmentioning

confidence: 98%

Identifying Key Factors in Determining Disparities in Energy Consumption in China: A Household Level Analysis

Yang

Zhao

et al. 2021

Energies

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With the rapid progress of socio-economic development and urbanization in China, a wide variety of literature has focused on the phenomenon of energy-consumption disparity, which in turn could be related to numerous fundamental energy dilemmas that China must deal with now or soon, including energy use inefficiency, regional energy shortage, insufficient use of green energy, etc. However, in most cases, only a tendency scenario is discussed, while identifying which factors are more likely to affect the improvement of energy supply/use has not yet been fully explored. Therefore, this paper attempts to explore differences in energy consumption in specific, household-level aspects. Based on the information provided by Chinese General Social Survey (CGSS2015), the Gini coefficient and the Lorentz asymmetry coefficient are used to measure the difference among various categories of energy type and end use within and between different geographical sub-groups. The findings show that: (1) household energy-consumption behaviors exhibit a complicated effect on the overall level of energy-consumption difference; (2) from the supply side, energy users show the most significant difference in biomass energy consumption, while from the demand side, the contribution of space heating to the difference in total energy consumption is the highest; (3) a great urban–rural difference in energy consumption is generally observed with its difference within rural areas being much greater than in urban areas; (4) the eastern, middle and western regions weight differently in explaining the overall difference of energy consumption. These findings provide meaningful materials and references for policymakers in China to understand the overall situation of individual energy consumption to a great extent, and to locate key points to reform the current energy policy framework.

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Estimating country-specific space heating threshold temperatures from national gas and electricity consumption data

Cited by 19 publications

References 36 publications

Satellite weather data accurately models global space heating and cooling demand

Satellite weather data accurately models global space heating and cooling demand

Replacing gas boilers with heat pumps is the fastest way to cut German gas consumption

Identifying Key Factors in Determining Disparities in Energy Consumption in China: A Household Level Analysis

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