Beyond building energy simulation tools

Abstract

Various energy simulation tools are used to predict energy consumption in buildings at different stages from design to post-occupancy and maintenance. The inaccuracy and insufficiency of inputs used for building energy simulation (BES) often cause a discrepancy between the predicted and actual energy consumption. Inaccurate energy consumption estimations affect the accomplishment of the sustainability goals and reduction of energy consumption and CO2 emissions in buildings. The review of the existing literature suggests that the potential causes of the aforementioned uncertainty in building energy predictions are divided into 2 categories: human error (in design, construction, energy modelling, etc.) and the inaccuracy and insufficiency of inputs in BES. This research proposes the way forward for BES tools to improve their accuracy by enhancing the precision of various energy simulation inputs, integration of real-time data and use of machine learning and other emerging technologies.