Global urbanization causes more environmental stresses in cities and energy efficiency is one of major concerns for urban sustainability. The variable importance techniques have been widely used in building energy analysis to determine key factors influencing building energy use. Most of these applications, however, use only one type of variable importance approaches. Therefore, this paper proposes a procedure of conducting two types of variable importance analysis (predictive and variance-based) to determine robust and effective energy saving measures in urban buildings. These two variable importance methods belong to metamodeling techniques, which can significantly reduce computational cost of building energy simulation models for urban buildings. The predictive importance analysis is based on the prediction errors of metamodels to obtain importance rankings of inputs, while the variance-based variable importance can explore non-linear effects and interactions among input variables based on variance decomposition. The campus buildings are used to demonstrate the application of the method proposed to explore characteristic of heating energy, cooling energy, electricity, and carbon emissions of buildings. The results indicate that the combination of two types of metamodeling variable importance analysis can provide fast and robust analysis to improve energy efficiency of urban buildings. The carbon emissions can be reduced approximately 30% after using a few of effective energy efficiency measures and more aggressive measures can lead to the 60% of reduction of carbon emissions. Moreover, this research demonstrates the application of parallel computing to expedite building energy analysis in urban environment since more multi-core computers become increasingly available.

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.