Different methods are used for calculating the energy consumption of buildings. While the heat balance method, weighing factor method and thermal network methods are complex and require intense computing power, the degree-day and the BIN methods are relatively simple. In this study, we have used the degree-day
approach published in CIBSE TM-41 to develop a Python tool to calculate the energy consumption of energy buildings on an hourly basis. The tool calculates the building energy for 59 Indian cities for five different cases: 1) fully air-conditioned, 2) with evaporative pre cooling, 3) evaporative cooling, 4) with comfort cooling and 5) with night ventilation with five different thermal comfort models. These cases are run for two building versions: 1) Business as Usual and 2) Energy Conservation Building Code compliant , and the study resulted in two realistic versions of Balance Point Temperatures for these cases. In total these add up to 2900 simulation which the Python tool completes within 1 hour. The results are shown in an excel dashboard.
The results indicate the cooling energy reduction possible due to the three passive design strategies. The CDD-based results are compared with EnergyPlus results. The Normalised Mean Biased Error is 4.6% while the Root Mean Squared Error value is 15%.
Keyword: Cooling Degree Days, Residual Cooling Degree Days, Passive strategies, Cooling energy, Balance Point Temperature Presented at: Building Simulation 2019, held at Rome
Supported by:
Ministry of New and Renewable Energy Govt. of India, Gujarat Energy Development Agency, U.S. Agency for International Development iNDEXTb (Industrial Extension Bureau) Govt. of Gujarat, Shakti Sustainable Energy Foundation, New Delhi
