Power saturation load prediction method and device

Abstract

The invention is suitable for the technical field of electric power, and provides an electric power saturation load prediction method and device, and the method comprises the steps: determining at least one key factor affecting an electric power saturation load, building a first prediction model based on the historical data of each key factor, and determining the prediction data of each key factoraccording to the first prediction model; establishing a second prediction model based on the historical data of each key factor and the historical data of the power consumption corresponding to the historical data of each key factor, and determining prediction data of the power consumption according to the second prediction model; and determining the time for reaching the power saturation load and the size of the power saturation load based on the prediction data of each key factor and the prediction data of the power consumption. The method can effectively improve the prediction accuracy ofthe power saturation load.

Description

Technical field[0001]The invention belongs to the field of electric power technology, and in particular relates to a method and device for predicting electric saturated load.Background technique[0002]Due to the development of the social economy, the expansion of the scale of cities, and the increase in power consumption, grid companies need to plan for grid development to meet the future power demand of the region. And forecasting the future saturated electric load in the region is an important part of the power grid development plan.[0003]However, with the gradual expansion of the construction scale of smart grids, big power data including production data, marketing data, and related socio-economic data have been formed. The traditional power load forecasting is based on total forecast, planned forecast and extrapolated forecast. Forecasting methods can no longer meet the requirements of power load characteristics analysis and forecasting under the new situation, and the accuracy o...

AI Technical Summary

Problems solved by technology

[0003] However, with the gradual expansion of the construction scale of the smart grid, electric power big data including production data, marketing data, and related social and economic data have been formed. The prediction method can no longer meet the requirements of the analysis and prediction of power load characteristics under the new situation, and the accuracy of the prediction results is poor

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