Abstract
Building energy performance tools are widely used to simulate the expected energy consumption of a given building during the operation phase of its life cycle. Deviations between predicted and actual energy consumptions have however been reported as a major limiting factor to the tools adopted in the literature. A significant reason highlighted as greatly influencing the difference in energy performance is related to the occupant behaviour of the building. To enhance the effectiveness of building energy performance tools, this study proposes a method which integrates Building Information Modelling (BIM) with artificial neural network model for limiting the deviation between predicted and actual energy consumption rates. Through training a deep neural network for predicting occupant behaviour that reflects the actual performance of the building under examination, accurate BIM representations are produced which are validated via energy simulations. The proposed method is applied to a realistic case study, which highlights significant improvements when contrasted with a static simulation that does not account for changes in occupant behaviour.
Highlights
An increasing number of governments around the world have set a target for reducing carbon emissions and enhancing the energy efficiency of buildings
Once the results from Insight360 are generated, they are passed on to the neural network of Figure 2 in order to predict the most appropriate occupant behaviour parameters that result in smallest deviation in energy consumption, as contrasted with actual monitoring data obtained for the HVAC system of the building
The analysis of the heating and cooling energy requirements is conducted over a specified number of periods; after several computational experiments, it was revealed that the best period for assessment of the overall consumption power in the building is one that is assessed every quarter, in line with how the energy consumption billing system is set up in Australia [86]
Summary
An increasing number of governments around the world have set a target for reducing carbon emissions and enhancing the energy efficiency of buildings. BIM and AI are integrated within the proposed framework to predict the energy consumption of the building, with the results contrasted with data collected from sensors and system logs present in the building. The work presented in this paper is organised as follows: first, a literature review on relevant elements of the proposed method, including existing building performance deviation assessment techniques, use of AI in building performance and adoption of BIM for building performance measure, is examined. In a bid to handle this challenge, Wang et al, have proposed the merging of data from sensors and external databases through a building identifier [30] When it comes to building performance, a great deal of research has been conducted in the realm of building performance management and the use of technology for enhancing the management of constructed facilities. The literature review is divided into three main areas, namely facility management and energy efficiency in building performance, use of AI in building performance and BIM for the management of building performance
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