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Applied Energy
Article . 2019 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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Building energy demand assessment through heating degree days: The importance of a climatic dataset

Authors: A. D'Amico; G. Ciulla; D. Panno; S. Ferrari;

Building energy demand assessment through heating degree days: The importance of a climatic dataset

Abstract

Abstract The weather is one of the main factors to consider when designing a building because it represents the most important boundary condition to affect the dynamic behaviour of the building. In the literature, many studies use the degree day to predict building energy demand. However, linking the results obtained from a generic building simulation tool with defined degree days, will not give reliable energy evaluation. The goal of this study is to demonstrate that the assessment of building energy demand through the use of the degree day is correct only if the determination of the climate index is a function of the same weather data. The relationship between Heating Degree-Day and heating energy performance was identified by determining some simple correlations, in order to obtain a preliminary evaluation of energy demands. The authors used Heating Degree Days based on three climate data-sets, developing different relationships and feedback. For the extraction of these correlations, numerous dynamic simulations on non-residential buildings characterized by high-energy performance were carried out. From the analysis of the results, it is clear that the relationships with higher correlation coefficients (higher than 0.9) are those that are a function of the degree calculated from the same climatic file used during the simulations. The proposed methodology, validated in this work for an Italian case study can be extended to any country and can be used to improve the reliability of any decision support tool based on climatic indexes.

Keywords

Heating energy demand, Degree days, Building thermal balance, Weather data, Building simulation model, Empirical correlations

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    96
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
96
Top 1%
Top 10%
Top 1%
Green