Short-term Load Analysis and Forecast Based on Big Data Technology
With the wide installation of smart meters and large-scale application of Customer Power Consumption Information Collection System,power user information has achieved "full coverage and full collection",and provided the data foundation for the user-based short-term load forecasting.Considering the large quantity of power consumers,data and calculation,this paper proposes customer load forecasting solution ideas based on big data technical architecture.On the problem of customer load analysis and factors impact on load,studies have been done to deal with large volumes of data,knowledge learning and data mining techniques to enhance the efficient use of forecast models to improve prediction accuracy.The application has verified the superiority of the proposed method to the traditional load forecasting method.
NETL
NSTL
万方数据
维普
