Artificial Intelligence Solutions Towards to BIM6D: Sustainability and Energy Efficiency

BIM6D is an aspect of building information modeling (BIM) that allows for a detailed analysis of a building's energy performance in order to improve energy and light efficiency, which in turn leads to a more sustainable building utilization. Predictions of a building's energy consumption can have added value in different aspects and for different building actors, be they engineers, architects or the building users themselves. The objective for this study is to explore mathematical and artificial intelligent approaches for predicting thermal energy consumption in buildings and to examine its use for BIM6D. The dataset used in the research includes several years of hourly thermal energy consumption collected in one block of Kaunas city. Experiments have been carried out using different forecasting methods. In terms of prediction accuracy, it is worth highlighting the Extra Trees with MAE < 3.5 kWh and Support vector regression (SVR) with MAE <= 2.63 kWh. However, Extra Trees seems to be the best in terms of MAPE (38.65%). Although prediction time is not the most critical parameter, it should be noted, that Extra Trees, SVR and auto-regressive models were found to be the most time-consuming (from 2 to 4 min) to linear models (<1 s) and extreme gradient boosting (similar to 3 s) and that these results may influence the selection of a model for real-life operation.