An analytical method for intelligent electricity use pattern with demand response

Intelligent electricity is one important part of the smart grid, and it lead to the new electricity utilization patterns of families, as the interaction mode between smart grid and users. This paper proposes an analytical method of users' electricity utilization behaviors for the future intelligent electricity utilization environment. First of all, based on the intelligent electricity scene, users' specific intelligent electricity behavior is analyzed. Then, according to the characteristics of intelligent power consumption behavior about the demand response, distributed generators and plug-in electric vehicles, the load model is built based on intelligent electricity. The paper proposes an analytical method for users' power behavior, which is based on pattern recognition. The feature selection method based on information measure is used. Considering the features of intelligent electricity behaviors, the heuristic forward sequence search strategy for feature optimization is used to select corresponding characteristics from the common feature space and compose the feature set. The fuzzy C-means clustering algorithm, the initial clustering center of which is improved, is used for different users' intelligent electricity behaviors clustering. In the numerical example, at first, we get the intelligent power simulation load via the load model, and adopt the proposed method in this paper to achieve users' electricity pattern recognition, aiming at the above simulation load. Then we get different electricity categories of users. Analyzing the characteristics of users category curves, we can match them with the corresponding users' behaviors. The experiment proves that the analytical method proposed in this paper, which is about users' electricity utilization behavior, can effectively identify users with different intelligent behaviors.