Indoor Temperature Validation of Low-Income Detached Dwellings under Tropical Weather Conditions

Abstract

Urban territorial expansion generated in the last decades has brought a series of consequences, such as the variation between urban and suburban weather conditions affecting indoor temperature and increasing electricity consumption derived from the use of cooling systems. Current approaches of simulation models in residential buildings use indoor environmental data for carrying out validations to propose hygrothermal comfort alternatives for the mitigation of the effects of the external environmental conditions on the interior spaces of dwellings. In this work, an hourly evaluation of both indoor and outdoor environmental parameters of two case studies in a tropical climate was carried out, by means of a whole-building simulation approach tool during a week representative of the warmest period of the year. The integration of the collected environmental data in the theoretical model allowed us to reduce the error range of the estimated indoor temperature with results in normalized mean bias error between 7.10% and −0.74% and in coefficient of variation of the root mean square error between 16.72% and 2.62%, in the different indoor zones of the case studies. At the same time, the energy assessment showed a difference of 33% in Case 1 and −217% in Case 2 for final electricity consumption.

Weather Files for the Calibration of Building Energy Models

“…Finally, those that depend on third-party companies are at the mercy of the accuracy of the algorithms used to interpolate the values of the meteorological variables of the site under study. The general recommendation to reduce the "energy performance gap" involves the use of on-site weather station data [32,33], but this is not always possible due to the high cost of the initial investment and its subsequent maintenance. There is the possibility of using a mixed weather file created from the optimal combination of meteorological variables from external companies and on-site weather stations since not all sensors have the same price, nor do they all have the same impacts in the simulation.…”