Research On Users’ Power Consumption Pattern Mining And Application Based On Fine-Grained Data

In the context of the rapid development of smart grids,the importance of demand side resources in the electricity market is increasingly prominent.On the one hand,as an important component of the power demand side,residential users have important practical significance in improving their management level by analyzing their power consumption behavior patterns,proposing targeted power consumption service strategies,and guiding them to participate in demand response.On the other hand,the load scale of a single user based on a household is small,and the usage status of any electrical appliance can affect the energy consumption of the entire household.However,traditional user load forecasting does not fully utilize the load characteristics of the user’s equipment level,resulting in low forecasting accuracy.To this end,this article first analyzes the behavior patterns of residential users’ electricity consumption,and then studies user level load forecasting algorithms.The main research contents and achievements include the following three points:(1)Taking user controllable equipment analysis as the research point,analyze the user’s electricity consumption behavior characterized by the operation of electrical equipment.Determine whether each electrical device is controllable based on its attributes,screen and analyze electrical devices that can reflect the user’s electricity consumption behavior,and define the one-to-one mapping relationship between each controllable device and the user’s behavior activities when it is in operation,laying a foundation for subsequent research.(2)A set of electricity consumption pattern mining and analysis methods suitable for residential users has been designed based on the analysis of daily electricity consumption sequences of users.Aiming at the daily use of electrical appliances by users in real life,statistical methods,clustering algorithms,and sequential association rule mining algorithms are used to mine the time distribution pattern of single electrical appliance operation status and the time series association pattern of multiple electrical appliances operation status in a user’s home,displaying the electricity consumption behavior patterns of residential users from multiple perspectives.(3)Taking residential user load forecasting as the research point,a load forecasting model considering user power consumption patterns is constructed.Firstly,a structure model of encoder and decoder based on bidirectional long and short term memory network is constructed to extract user power consumption patterns and make initial predictions;Then,an error correction model based on feedforward neural network is constructed to correct the initial prediction value.The experimental results show that compared to TCN and LSTM models,the MAPE index value of this model decreases by an average of 10.1%,and the prediction effect is significantly improved.On the one hand,the research results of this article enrich the analytical methods for mining user power consumption patterns,and on the other hand,verify the application value of user power consumption behavior patterns in the field of user level load forecasting,providing support for promoting the optimal utilization of demand side resources and reducing household energy consumption.