Electricity Demand Prediction of Smart Grid in Smart Cities Using K-means Algorithm

Abstract

The aims are to unify big data management among various departments in smart city construction, establish a centralized data collection and sharing system, and provide fast and convenient big data services for smart applications in various fields. The national grid industry is taken as the research object. A new electricity demand prediction model is proposed based on smart city big data’s characteristics. This model integrates meteorological, geographic, demographic, corporate, and economic information to form an intelligent big database. The K-mean algorithm mines and analyzes the data to optimize the electricity user information. The electricity prediction model is established using the Backpropagation (BP) neural network algorithm. The electricity market is evaluated through an in-depth exploration of data relationships to verify the effectiveness of the model proposed. Results demonstrate that the K-mean algorithm can significantly improve electricity user segmentation accuracy, separate the different regional electricity consumption, and categorize different electricity users. The electricity demand network model constructed can significantly improve the prediction accuracy, and the mean error rate is 3.2671%. The model’s training time improved by the additional momentum factor is significantly reduced, and the mean error rate is 2.13%. The above results can provide a theoretical and practical basis for electricity demand prediction and personalized marketing, as well as development planning for the electricity sector.