The spatial feature of building energy consumption in a city is essential for urban level energy planning and policy making. With the increasing availability of urban level building energy benchmarking datasets, machine learning has shown a powerful capability of making data-driven predictions on urban level building energy consumption. However, the building energy benchmarking datasets usually only cover large buildings, which are not sufficient representations of all buildings in a city. Besides building energy benchmarking datasets, many other urban level open datasets are also valuable to building energy prediction, but they do not contain building energy use data, in other words, they are unlabeled. This study proposes a novel framework based on semi-supervised learning to make effective use of the unlabeled datasets to develop more generic urban level data-driven building energy prediction models, and energy mapping with higher space resolution. The framework consists of preliminary labeling, selection of pseudo labeled samples and predictive modelling. Several machine learning algorithms are proposed and compared for generating pseudo labels of building electricity consumption for unlabeled datasets of small and medium-sized buildings. A selection process consisting of convergence testing and screening is designed to select pseudo labeled samples with high credibility to enlarge the labeled dataset. A novel two-level performance evaluation method is proposed to evaluate the performance of the framework at both urban level and district level to enhance the spatial resolution of the predictions. The framework is implemented to model and map the individual electricity consumptions of all buildings in two years in the districts of New York City using multiple open datasets. The results show significant improvement in terms of prediction accuracy at both levels. In addition, the applicability of the model to various buildings in the city is remarkably enhanced.
城市建筑能源建模；建筑用电量；公开数据；半监督学习；可信度测量
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