6533b7d1fe1ef96bd125c359
RESEARCH PRODUCT
Building energy demand assessment through heating degree days: the importance of a climatic dataset
A. D'amicoDomenico PannoGiuseppina CiullaSimone Ferrarisubject
Decision support systemComputer science020209 energymedia_common.quotation_subject02 engineering and technologyManagement Monitoring Policy and LawDegree (temperature)Heating energy demandDegree day020401 chemical engineering0202 electrical engineering electronic engineering information engineeringSettore ING-IND/10 - Fisica Tecnica Industriale0204 chemical engineeringFunction (engineering)Reliability (statistics)media_commonHeating energy demand Degree days Building thermal balance Weather data Building simulation model Empirical correlationsSettore ING-IND/11 - Fisica Tecnica AmbientaleDegree dayMechanical EngineeringWork (physics)Building simulation modelBuilding and ConstructionEmpirical correlationsIndustrial engineeringGeneral EnergyEnergy (all)Weather dataEmpirical correlationBuilding thermal balanceDegree daysHeating degree dayEnergy (signal processing)description
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-05-01 |