Energy characteristics of urban buildings: Assessment by machine learning

Abstract

Machine learning techniques have attracted more attention as advanced data analytics in building energy analysis. However, most of previous studies are only focused on the prediction capability of machine learning algorithms to provide reliable energy estimation in buildings. Machine learning also has great potentials to identify energy patterns for urban buildings except for model prediction. Therefore, this paper explores energy characteristic of London domestic properties using ten machine learning algorithms from three aspects: tuning process of learning model; variable importance; spatial analysis of model discrepancy. The results indicate that the combination of these three aspects can provide insights on energy patterns for urban buildings. The tuning process of these models indicates that gas use models should have more terms in comparison with electricity in London and the interaction terms should be considered in both gas and electricity models. The rankings of important variables are very different for gas and electricity prediction in London residential buildings, which suggests that gas and electricity use are affected by different physical and social factors. Moreover, the importance levels for these key variables are markedly different for gas and electricity consumption. There are much more important variables for electricity use in comparison with gas use for the importance levels over 40. The areas with larger model discrepancies can be determined using the local spatial analysis based on these machine learning models. These identified areas have significantly different energy patterns for gas and electricity use. More research is required to understand these unusual patterns of energy use in these areas.