Further validation of a method aimed to estimate building performance parameters |
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| Affiliation: | 1. Clinical Unit of Cardiomyopathy and Aortic Diseases, Heart Institute (InCor) – Hospital das Clínicas da Faculdade de Medicina – Universidade de São Paulo, São Paulo, Brazil;2. Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA;3. Clinical Unit of Electrocardiography, Heart Institute (InCor) – HC-FMUSP, São Paulo, Brazil;4. Hypertrophic Cardiomyopathy Department, Heart Institute (InCor) – HC-FMUSP, São Paulo, Brazil;1. KU Leuven, Department of Civil Engineering, Building Physics Section, Belgium;2. VITO, Unit Smart Energy and Built Environment, Belgium;3. EnergyVille, Cities in Transition Section, Belgium;4. University of Lincoln, School of Architecture and the Built Environment, UK;5. University of Antwerp, Applied Engineering, EMIB, Belgium |
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| Abstract: | A further validation of an earlier developed neural network method for estimating the total heat loss coefficient (Ktot), the total heat capacity (Ctot) and the gain factor (α) based on measured diurnal data of internal–external temperature difference, supplied heat for heating and “free heat” is presented. The validation was performed in laboratory scale, using a test cell, for three different cases of ventilation, without (constant)-, natural-, and forced ventilation. Earlier measurements from a building was also used in order to simulate a realistic energy use pattern and a rather stochastic behavior of α, which also was transformed to represent existing and future buildings in terms of the composition of their energy use. For all three types of ventilation and different types of buildings, the method was capable of estimating the three different performance parameters and their different dependencies. For Ktot, the RMSE was between 3 and 20% and for α, the deviation was between 9 and 19%. |
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