Population-weighted degree-days: The global shift between heating and cooling

Kennard, Harry; Oreszczyn, Tadj; Mistry, Malcolm; Hamilton, Ian

doi:10.1016/j.enbuild.2022.112315

Cited by 10 publications

(2 citation statements)

References 21 publications

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“…In contrast to Heating Degree Days (HDDs), which are calculated when the average outdoor temperature falls below a "base temperature" (termed as "heating temperature limit" by VDI 4710 [50]) and refers to a certain location [51,52], climate factors encompass a wide range of meteorological variables (temperature, humidity, solar radiation, etc.) that describe general weather patterns and climatic conditions relative to the reference climate [48].…”

Section: Climate Factormentioning

confidence: 99%

Factoring Building Refurbishment and Climatic Effect into Heat Demand Assessments and Forecasts: Case Study and Open Datasets for Germany

Salaymeh,

Peters,

Holler

2024

Energies

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Reducing the heat demand of existing buildings is an essential prerequisite for achieving a greenhouse gas-neutral energy supply. Numerous studies and open-source tools deal with heat demand mapping. It is not uncommon that estimated heat demands deviate from real heat consumption, so existing approaches should be improved by including in-depth building information. Some tools have recognised this problem and offer built-in functions for factoring various parameters into their assessments. Nevertheless, the necessary information is usually missing and should be obtained first. In this paper, we analyse the impact of thermal refurbishment and climate on building heat demand; hence, generate public datasets with corresponding key figures for each building type in different efficiency states and years. Accounting for already performed refurbishments in methodologies for assessing the actual state heat demand for cities will result in a reduction of at least 8% up to more than 21%, depending on whether conventional or passive house components were installed. As a result of climatic differences within Germany, a building’s heat demand can be up to 39% higher or up to 21% lower than the heat demand of an identical building in the reference climate of Germany. By further developing the approaches of the tools Hotmaps and Heat Cadastre Hamburg, we could improve the estimated heat demand of Hamburg to a value approximating the real consumption.

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Section: Climate Factormentioning

confidence: 99%

Factoring Building Refurbishment and Climatic Effect into Heat Demand Assessments and Forecasts: Case Study and Open Datasets for Germany

Salaymeh,

Peters,

Holler

2024

Energies

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“…Despite extensive research confirming the individual-level effects of extreme heat on older adults’ health and mortality risk 11 , older adults’ population-level heat exposure has received less attention 2 , 5 . Coincident trends of population aging and a warming climate portend the emergence of biologically and socially vulnerable “hotspots”: countries and regions that experience both increasing concentrations of older adults and intensifying high temperature extremes 12 , 13 . The global population is aging at an unprecedented pace.…”

Section: Introductionmentioning

confidence: 99%

Global projections of heat exposure of older adults

Falchetta,

De Cian,

Sue Wing

et al. 2024

Nat Commun

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The global population is aging at the same time as heat exposures are increasing due to climate change. Age structure, and its biological and socio-economic drivers, determine populations’ vulnerability to high temperatures. Here we combine age-stratified demographic projections with downscaled temperature projections to mid-century and find that chronic exposure to heat doubles across all warming scenarios. Moreover, >23% of the global population aged 69+ will inhabit climates whose 95th percentile of daily maximum temperature exceeds the critical threshold of 37.5 °C, compared with 14% today, exposing an additional 177–246 million older adults to dangerous acute heat. Effects are most severe in Asia and Africa, which also have the lowest adaptive capacity. Our results facilitate regional heat risk assessments and inform public health decision-making.

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An energy consumption rectification method based on Bayesian linear regression and heating degree‐days

Sun,

Wang,

Sun

et al. 2024

Energy Science & Engineering

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The time‐varying external environment is one of the main variables influencing heating energy consumptions, so that its influence should be rectified when energy savings of different heating modes are calculated. This paper proposes an energy consumption rectification method based on Bayesian linear regression and heating degree‐days, to obtain heating energy consumptions without the influence of different outdoor temperatures. The proposed method consists of three main steps. First, a physical model of heating houses is used to prove a relationship between energy consumptions and heating degree‐days. Second, Bayesian linear regression is exploited to estimate uncertainty ranges of heating energy consumptions. Finally, heating energy consumptions are rectified, and energy savings with their uncertainty ranges for different heating modes under the same outdoor temperature are obtained. The proposed method does not require the physical parameters of heating houses to facilitate practical implementation. Additionally, it provides uncertainty ranges of heating energy consumptions to measure the estimation accuracy. Numerical and experimental examples show that the proposed method provides more accurate estimates of heating energy consumptions than existing methods.

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Population-weighted degree-days: The global shift between heating and cooling

Cited by 10 publications

References 21 publications

Factoring Building Refurbishment and Climatic Effect into Heat Demand Assessments and Forecasts: Case Study and Open Datasets for Germany

Factoring Building Refurbishment and Climatic Effect into Heat Demand Assessments and Forecasts: Case Study and Open Datasets for Germany

Global projections of heat exposure of older adults

An energy consumption rectification method based on Bayesian linear regression and heating degree‐days

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