Prediction and Analysis of Power User Energy Consumption Based on Demand Side Management

Due to the huge imbalance between power supply and demand and seasonal climate change, it is easy to cause huge waste. Therefore, the demand response based on residential building energy consumption prediction will play an important role in the power demand side management mechanism. Therefore, this paper proposes a model to predict the power consumption level of residential buildings in the whole area based on the basic data of power consumption. Firstly, the model uses data mining technology to find and summarize the hidden power consumption patterns in the data. Secondly, particle swarm optimization k-means algorithm is applied to cluster analysis, and the power consumption level is divided by the cluster center. Finally, an efficient classification model based on support vector machine is proposed. The test shows that the prediction performance of the model is much better than the traditional method, and has a certain reference value for rational allocation of power supply capacity and improving the overall quality of power grid.