Research On Application Of Data Mining In Electric Power Demand Forecast

Electricity demand forecast is an important part of the power system planning and operation,the power companies to develop power generation, power purchase plan, an important basis toensure grid security, economic operation, therefore accurate prediction of future electricitydemand is a very important issue.Based on the electricity demand of Jilin hydropower company forecast softwareimplementation as an example, introduces the application of electricity demand in the companycomprehensive prediction model based on data mining..First introduced the concept of forecasts of electricity demand and classification, as well asforecasts of electricity demand Research, research and analysis of the various factors affectingthe demand for electricity, Survey of Jilin Hydro electricity demand forecast and demandresearch, summed up the many problems and defects in the original system; the new electricitydemand forecasting system design requirements. Integrated data mining library, on the the Jilinhydropower subordinate basis of historical data (such as power marketing system of electricitytariff data, the history of the load system daily load data, etc.) through the establishment of ascientific and effective electricity demand forecast for the collection and extraction, will becollected to the data processing data preprocessing, eliminate abnormal data and redundant data,noise data, the data integration and conversion and the Statute, so that the final data to meet therequirements of data mining. Finally, the new electricity demand forecasting system functionaldesign, and analysis and comparison of the one-sided and shortcomings of conventionalelectricity demand forecasting model, the concept of comprehensive prediction, the predictionmodel based on an integrated design ideas. And use the integrated prediction model society as awhole of the company’s electricity consumption instance prediction, the prediction accuracy ofthe model results show that the design requirements, in line with forecasts of electricity demandin order to verify the effectiveness and feasibility of the model, can help companies effectivelypower grid planning, rational allocation of grid resources, improve power supply reliability rate,to improve power enterprise return on investment, and ultimately the company economic andsocial benefits double harvest.