Establishing the relationship between indoor and outdoor temperature of an existing office building using hybrid physics based and data driven models

Abstract

Building cooling energy demand accounts for a significant fraction of the global energy demand. This is significant in hot and humid countries especially in the South Asian region where the electricity infrastructures are at risk due to increasing demand for building cooling due to various factors such as global warming and growth of population. Although this risk has been identified, limited research has been conducted in energy demand predictions in these climatic zones. Nevertheless, the use of data driven modelling for demand predictions of buildings is becoming popular around the world. Hence, this paper focuses on developing a model to predict indoor temperature from outdoor conditions to estimate the cooling energy demand. The model presented is a hybrid model which is a combination of physics based and data driven models. The model was trained and validated for an existing office building in a tropical climate zone. Model parameters were calculated using different surveys. The developed model has been used to calculate the cooling energy demand of the selected zones. It was observed that the demand increase is dependent on various zone envelope properties (window area, floor area etc.) and these zones show a significant correlation between indoor and outdoor conditions.