Comparative Evaluation of Threshold Modelling for Smart Buildings’ Performance Testing

Abstract

With buildings consuming ca. 40% of the world’s total energy consumption, greater importance is given to their performance and ensuring that they behave as originally intended. The key to timely detection of underperformance is continuous real time measurement of a building’s behavior. To this end, performance testing has been developed as a practice that compares the observed behavior and the expected behavior of a building. Representation of the observed behavior is obtained by applying specific calculations to meters’ and sensors’ readings. The expected behavior can be calculated in different ways, depending on the necessity for historical data, or knowledge regarding the physical relationships between the building components. We study and compare these approaches based on the difficulty to develop and use, accuracy in predicting the expected behavior, as well as their ability to be integrated and run in real-time. The models are additionally compared to the country’s regulations for building energy consumption. The models for simulating the energy consumption of a building are trained and calibrated based on data from a case study smart building located in Denmark. The results show the superiority of the black box model, based on the higher accuracy of the forecasted performance, the lower effort of model generation and simulation, as well as applicability to a variety of buildings.