Interval Estimations of Building Heating Energy Consumption using the Degree-Day Method and Fuzzy Numbers

Cheng, Xin; Li, Simon

doi:10.3390/buildings8020021

Cited by 10 publications

(3 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A typical household's carbon emission is 78% higher in Memphis than in San Diego [161]. Cheng and Li [162] found that household energy consumption is strongly related to heating degree days (HDDs). This relation between household energy consumption and HDDs is also reported in China, Japan, the USA, and Canada [163], Turkey [164], and France [165].…”

Section: Influential Factors On Household Energy Consumption and Co 2 Emissionsmentioning

confidence: 99%

An Overview of Household Energy Consumption and Carbon Dioxide Emissions in Iran

et al. 2020

View full text Add to dashboard Cite

This review tends to obtain a deeper understanding of the methods used in household energy consumption and carbon dioxide (CO2) emissions in Iran. Issues relating to energy consumption and CO2 emissions are very complex. This complexity arises from the fact that energy demand and energy consumption in Iran are influenced by many factors, such as income, household size, age, and gender. In Iran, the relevant energy sources mostly include liquefied petroleum gas (LPG) and electricity, which are used for different sectors, such as transportation, industry, and residential. This overview looks at both the theories and empirical studies of household energy consumption and CO2 emissions in Iran. Since energy consumption typically results in air pollution, it is often used as an indicator of environmental degradation. Although Iran is recently faced to energy efficiency improvement from all sectors, household energy requirements have been significantly increased. In Iran, a prime motivator had been improving living standards. As Iran gradually turns into a consumer society, households have an enormous influence on the direct use of energy and related CO2 emissions as well as through indirect use, as embodied in goods and services. The findings of this study can help policymakers to focus on renewable energy projects in order to reduce energy consumption and mitigate CO2 emissions.

show abstract

Section: Influential Factors On Household Energy Consumption and Co 2 Emissionsmentioning

confidence: 99%

An Overview of Household Energy Consumption and Carbon Dioxide Emissions in Iran

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In general, daily average temperature values are used for the building energy planning (e.g., Matzarakis and Balafoutis, 2004, Christensen et al, 2006, Mourshed, 2016, Cheng and Li, 2018, including the heating system design parameters. Because of its common use, we also used daily temperature data for Budapest.…”

Section: Datamentioning

confidence: 99%

Analysis of heating and cooling periods in Budapest using station data

Dian¹,

Talamon²,

Pongrácz³

et al. 2021

Időjárás

View full text Add to dashboard Cite

The built environment has a very complex role in cities. On the one hand, various urban climatological phenomena are caused and influenced by buildings (e.g., urban heat island effect, local wind conditions, air pollution). On the other hand, buildings are important contributors to energy use via heating and cooling, e.g. they account for about 40% of total energy consumption on average in Europe. Daily average outdoor temperature is taken into account to design the heating and cooling systems of residential, commercial, or office buildings. That is why we analyzed the available temperature time series of the capital of Hungary, Budapest for the period between 1901 and 2019. The aims of this study are (i) to investigate the changes in temperature data series that influence building energy design parameters, (ii) to analyze the heating and cooling periods in the last 119 years based on different definitions, and (iii) to define a third (transitional) period between the heating and cooling periods. Based on the results, it can be concluded that the variability of warm days is smaller than that of cold days, consequently, the optimal design of heating systems is a greater challenge compared to cooling systems. Furthermore, the length of the temperature-based heating period decreased substantially, while the length of the cooling period increased as a consequence of overall regional warming.

show abstract

“…In Ref. [23] a new concept (infiltration degree-days (IDD)) was proposed, which added a weighting factor calculated from relative infiltration. Huang [24] correlated residential building heating and cooling loads with different climatic parameters based on hourly simulated data.…”

Section: Introductionmentioning

confidence: 99%

Change-point multivariable quantile regression to explore effect of weather variables on building energy consumption and estimate base temperature range

Meng

Xiong

Mourshed

et al. 2020

Sustainable Cities and Society

View full text Add to dashboard Cite

Mean regression analysis may not capture associations that occur primarily in the tails of the outcome distribution.In this study, we focused on building heating gas consumption related to multiple weather factors to find the extent to which they impact gas consumption at higher quantile levels. We used change-point multivariable quantile regression models to investigate distributional effects and heterogeneity in the gas consumption-related responses to weather factors. Subsequently, we analyzed quantile regression coefficients that corresponded to absolute differences in specific quantiles of gas consumption associated with a one-unit increase in weather factors. We found that the association of weather factors and gas consumption varied across 19 quantiles of gas consumption distribution. The heterogeneity of the case-study buildings was different: right tails of gas consumption for the community (CL) and educational (ED) buildings were more susceptible to weather factors than those of the health care (HL) building. The base temperature of the CL building across quantiles of gas consumption indicated a flat trend, but the uncertainty ranges were relatively large compared with those for the CL and ED buildings. This study could reveal which factors are most important and the extent to which they affect gas consumption at specific quantile levels.

show abstract

Interval Estimations of Building Heating Energy Consumption using the Degree-Day Method and Fuzzy Numbers

Cited by 10 publications

References 47 publications

An Overview of Household Energy Consumption and Carbon Dioxide Emissions in Iran

An Overview of Household Energy Consumption and Carbon Dioxide Emissions in Iran

Analysis of heating and cooling periods in Budapest using station data

Change-point multivariable quantile regression to explore effect of weather variables on building energy consumption and estimate base temperature range

Contact Info

Product

Resources

About