Power Load Forecasting And Its Deviation Warning Based On Data Mining

With the development of smart grids,terminal measurement equipment represented by smart meters has been widely used.It is worth pointing out that the measurement data collected by the smart meter,in addition to being used for electricity metering,also contains a large amount of social value.In order to obtain these hidden values,the theory of data mining has been gradually applied to the analysis process of power load.For power load data,the use of historical data to predict future loads is one of its main applications.At the same time,the "White Paper on China's Power Big Data Development" also pointed out that big data technology will bring new development space for the development of the power industry.In this paper,based on the problem of power user load forecasting,the data mining technique is used to decompose the above problem.Specifically,firstly,this paper proposes a load index extraction technology based on power K-line diagram,which is modeled after the financial secondary market.For the K-line analysis technology,it uses the power load index to establish an analysis system,the system mainly contains two main parameters,namely KDJ indicator and MACD indicator.Combined with the actual load data,it is proved that the power K-line indicator can be used for load analysis.Secondly,a power load data prediction model based on deep neural network method is proposed.Specifically,this paper proposes a deep neural network based on the auto-encoder and BP neural network.Based on the neural network,the effectiveness of the prediction algorithm is verified by an example.Finally,a bias warning method based on hidden Markov process is proposed.This method can pre-verify the accuracy of each prediction model under the condition of unknown real load on the predicted day.