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Energy-efficient urban design is an important prerequisite to sustainable urban development and reduction of greenhouse gas emissions. This study proposes an automatic framework to optimize urban design through the use of an urban building energy model. Three optimization goals were defined: maximum solar energy utilization, solar lighting of the first floor, and minimum building energy demand. Urban morphology was integrated into the optimization process as the bridge between the urban design scenario and the actual urban block. To validate the model, this study abstracted basic urban forms from actual urban contexts to generate urban blocks with the Rhino tool and run optimization in the Wallacei X, for multi-objective optimization in Rhino. The long short-term memory (LSTM) network was applied to infer energy performance of 41 actual urban blocks in Jianhu, China. In the results, the proposed framework can be validated feasibly with optimization of 100 iterations. A set of optimal results will be achieved for three goals and five clusters defined for different concerns of urban design strategies. The LSTM can achieve the best accuracy of 1.21% and 1.37% for energy generation of photovoltaic and total building energy use intensity respectively. • Low-energy urban design is important to sustainable urban development. • Enhancing solar utilization and reducing energy demand are important in urban design. • Urban building energy modeling is a popular and inspiring tool in urban design. • This study proposes an automatic low-energy urban design using simulation and data-driven approach. • The framework is validated feasibly by actual urban design and real urban blocks. [ABSTRACT FROM AUTHOR]

Buildings are one of the most important energy use sectors in cities, and forecasting the abnormal increase in building energy demand in certain climatic conditions is necessary to adjust building energy operations and implement energy policy. Accordingly, this research proposes a data-driven abnormally high energy demand detection framework in urban buildings based on their design parameters and local weather data, with the support of machine learning techniques. In this study, 71 public buildings with energy records in Jianhu city, Jiangsu province, China, were selected to abstract urban morphologies at community scale. The weather profile for the city was obtained from year 2015–2018 to create weather characteristics. Three machine learning algorithms—random forest, support vector machine, and artificial neural network—were applied to identify the months of abnormally high electricity consumption in different building types. This framework also explores key variables in the data and provides the basis for a system that prioritizes the acquisition of variables when complete data is unavailable. The results show that, with complete data, the accuracy score of the system in this study can reach 0.854 with the SVM algorithm, and the model returned an accuracy of 0.865 with the RF model after the key variable selection. Based on those results, the framework in this study can generate preemptive warnings for months with an expected abnormally high energy consumption in target buildings as a prerequisite of energy policy. • A data-driven abnormally high electricity use detection system is proposed. • Different morphologies under different weather conditions were tested. • Key factors of urban morphologies and weather conditions were identified. • Three machine learning techniques were applied for the prediction in the system. • This study can be applied for energy management in building and urban scales. [ABSTRACT FROM AUTHOR]