Power load forecasting method, device, computer equipment and storage medium

Abstract

The present application relates to a power load forecasting method, device, computer equipment, and storage medium. The method includes: responding to a power load forecast request, acquiring power load-related characteristic data corresponding to the power load forecast request, including historical power load and further divided into characteristic data related to domestic electricity load and characteristic data related to production electricity load; then input the pre-built electricity load difference prediction model to obtain the predicted difference of domestic electricity load corresponding to the forecast time information and The production power load forecast difference, and the historical power load value is calculated and processed to obtain the power load forecast result. In this application, through the characteristic data related to the domestic electricity load and the characteristic data related to the production electricity load, the use of the model is used to perform separate predictions, which improves the precision of the electricity load prediction; variance, which improves the accuracy of electric load forecasting.

Description

technical field [0001] The present application relates to the field of electric power technology, and in particular, relates to a power load forecasting method, device, computer equipment and storage medium. Background technique [0002] With the digitalization, intelligence, and modernization of power systems, power load forecasting plays an increasingly important role in the construction of smart grids. On the one hand, accurate power load forecasting can provide valuable data support for the power system on the supply side and demand side; on the other hand, accurate power load forecasting can reduce the cost of power exchange. [0003] The existing power load forecasting methods mainly rely on data such as weather, time, and historical load values; traditional statistical analysis models are used to predict power loads in a short period; however, the power load value itself is relatively large, and the power load value fluctuates frequently , resulting in large variance...

AI Technical Summary

Problems solved by technology

[0003] The existing power load forecasting methods mainly rely on data such as weather, time, and historical load values; traditional statistical analysis models are used to predict power loads in a short period; however, the power load value itself is relatively large, and the power load value fluctuates frequently , leading to large variance in power load forecasting and poor accuracy of forecasted data

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