Power User Behavior and Load Forecasting Based on Big Data

With the improvement of power system intelligence and the development of power collection technology, a huge amount of power data is generated. This data includes the number of times of electricity consumption, time of electricity consumption, electricity equipment and other users' electricity consumption behaviours. In this paper, a set of power consumption behavior analysis model based on power big data was constructed, which helped to deeply understand the characteristics and behavioral laws of users' power consumption, and provided important support for the energysaving management and optimization of China's power grid enterprises. Traditional research on electricity consumption behaviour mainly relies on small-sample research and simple statistical analysis, which cannot completely and accurately grasp electricity consumption behaviour. However, the electric power consumption collection and analysis model based on electric power big data, with its large data scale, multiple dimensions, wide coverage and high accuracy, can study users' electricity consumption behaviour from a finer and more comprehensive perspective. In this paper, a user electricity consumption behaviour analysis model based on electricity big data was introduced, and the K-SVD (K-Singular Value Decomposition) algorithm was used to decompose the user's electricity consumption patterns in order to extract the user's electricity consumption characteristics and behavioural patterns. The experimental results showed that the K-SVD algorithm was able to predict the power consumption of users more accurately, and the prediction error was small, usually between 1-2 kWh. Load prediction based on the K-SVD algorithm shows high efficiency and stability in practical applications, providing strong support for energy management and optimization.