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

Abstract

The invention relates to a power load prediction method and device, computer equipment and a storage medium; and the method comprises the steps: in response to a power load prediction request, obtaining power load related feature data corresponding to the power load prediction request, the power load related feature data comprising historical power load values; further dividing the feature data into life power load related feature data and production power load related feature data; and then inputting a pre-constructed power load difference prediction model to obtain a life power load prediction difference and a production power load prediction difference corresponding to the prediction time information, and performing calculation processing on the historical power load values to obtain anpower load prediction result. According to the invention, through the life power load related feature data and the production power load related feature data, respective prediction is carried out byusing the model, and the fine degree of power load prediction is improved; the variance in the prediction process is reduced by predicting the power load difference value, and the accuracy of power load prediction is improved.

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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