The safety and reliability of urban power supply are critical to the sustainability of cities and society. Based on the analysis of big data, a power supply reliability evaluation method for urban distribution networks considering uncertain factors is proposed in this paper. The method has good adaptability and can support the analysis of safety improvement measures. By investigating historical data on distribution network topology and parameters, the main influencing factors affecting power supply reliability and the uncertainties of these factors are screened out. An improved Elman neural network (IENN) is used, and the main process of reliability evaluation is obtained for the complex urban distribution network. It can effectively simplify the calculation and includes multiple uncertain factors to improve evaluation accuracy. Case studies with actual urban distribution network data are used to verify the feasibility and effectiveness of the proposed method. Finally, some useful conclusions are given, including the problems of urban distribution network power supply, and the improvement measures for power supply to support the development of sustainable cities and society.

城市供电的安全性和可靠性对于城市和社会的可持续性至关重要。在大数据分析的基础上，提出了一种考虑不确定因素的城市配电网供电可靠性评估方法。该方法适应性强，可以支持对安全改进措施的分析。通过调查配电网拓扑和参数的历史数据，筛选出影响供电可靠性的主要影响因素以及这些因素的不确定性。使用改进的埃尔曼神经网络（IENN），获得了复杂城市配电网可靠性评估的主要过程。它可以有效简化计算，并包含多个不确定因素以提高评估准确性。以实际城市配电网数据为例，验证了该方法的可行性和有效性。最后，给出了一些有益的结论，包括城市配电网供电问题，以及支持可持续城市和社会发展的供电改进措施。