This work aims to perform the unified management of various departments engaged in smart city construction by big data, establish a synthetic data collection and sharing system, and provide fast and convenient big data services for smart applications in various fields. A new electricity demand prediction model based on back propagation neural network (BPNN) is proposed for China?s electricity industry according to the smart city?s big data characteristics. This model integrates meteorological, geographic, demographic, corporate, and economic information to form a big intelligent database. Moreover, the K-means clustering algorithm mines and analyzes the data to optimize the power consumers? information. The BPNN model is used to extract features for prediction. Users with weak daily correlation obtained by the K-means clustering algorithm only input the historical load of adjacent moments into the BPNN model for prediction. Finally, the electricity market is evaluated by exploring the data correlation in-depth to verify the proposed model?s effectiveness. The results indicate that the K-mean algorithm can significantly improve the segmentation accuracy of power consumers, with a maximum accuracy of 85.25% and average accuracy of 83.72%. The electricity consumption of different regions is separated, and the electricity consumption is classified. The electricity demand prediction model can enhance prediction accuracy, with an average error rate of 3.27%. The model?s training significantly speeds up by adding the momentum factor, and the average error rate is 2.13%. Therefore, the electricity demand prediction model achieves high accuracy and training efficiency. The findings can provide a theoretical and practical foundation for electricity demand prediction, personalized marketing, and the development planning of the power industry.

Thunder is a discharge phenomenon that often occurs in nature. Due to its physical influences such as strong current, high temperature, strong shock waves, and strong electromagnetic radiation, it has a huge destructive effect instantly, which may bring serious threats to people’s lives and property safety. This study aimed to study the lightning discharge numerical simulation and active protection based on the quantum heuristic evolutionary algorithm and proposed to apply the lightning discharge numerical simulation to the prevention of lightning disasters. This article gives a detailed description of the quantum algorithm, the generation, and harm of lightning discharge. The genetic algorithm is used to optimize the lightning data simulation algorithm, and the optimization process is introduced in detail. In addition, this article conducts related experiments on lightning discharge numerical simulation and active protection. The experimental results show that targeted active protection and effective numerical simulation are important measures to prevent lightning disasters. Active lightning protection measures can reduce lightning by 30%. Losses are caused by disasters.

Thunder is a discharge phenomenon that often occurs in nature. Due to its physical influences such as strong current, high temperature, strong shock waves, strong electromagnetic radiation, etc., it has a huge destructive effect instantly, which may bring serious threats to people's lives and property safety. This paper aims to study the lightning discharge numerical simulation and active protection based on the quantum heuristic evolutionary algorithm, and proposes to apply the lightning discharge numerical simulation to the prevention of lightning disasters. This article gives a detailed description of the quantum algorithm, the generation and harm of lightning discharge. In addition, this article conducts related experiments on lightning discharge numerical simulation and active protection. The experimental results show that targeted active protection and effective numerical simulation are important measures to prevent lightning disasters. Active lightning protection measures can reduce lightning by 30%. Losses caused by disasters.