Building energy demand assessment through heating degree days: The importance of a climatic dataset. (15th May 2019) - D'Amico, A.; Ciulla, G.; Panno, D.; Ferrari, S.
Graphical abstract: Highlights: Thermal energy demand evaluation for buildings with high-energy performance. Application of the Degree Days method for the evaluation of building heating load. Several dynamic building simulations located in different cities were considered. A comparison of the results obtained using different weather data sets was done. Reliable relationships for preliminary of heating demand assessment were provided. 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.