Prediction model training method and device, prediction method and device, equipment and medium

Abstract

The invention discloses a prediction model training method and device, a prediction method and device, equipment and a medium. The training method comprises the steps of obtaining a sequence value ofa historical power load and a plurality of influence factors corresponding to the sequence value; extracting power load characteristics and influence factor characteristics from the sequence values and the influence factors; and training a prediction model based on a CatBoost algorithm by using the power load characteristics and the influence factor characteristics, with the prediction model beingused for predicting the power load of the prediction target in the next time period. According to the embodiment of the invention, the power load characteristics and the influence factor characteristics are extracted from the obtained historical sequence values and the corresponding influence factors; and the extracted power load features and influence factor features are trained to obtain a prediction model, so that feature processing in the training process is simple, the prediction model obtained by training is accurate in short-term power load prediction of enterprise users, and a reliable basis is provided for power spot market transaction.

Description

technical field [0001] The present application generally relates to the field of computer technology, and in particular relates to a forecasting model training method, forecasting method, device, equipment and media. Background technique [0002] In the industry, the power load required by enterprise users in the production process is usually purchased from power sales companies based on medium and long-term power loads, and power sales companies report the future medium and long-term power consumption from the power generation side in advance. In practice, the short-term power demand of industrial enterprise users does not necessarily meet the pre-plan. At this time, electricity sales companies need to sell or supplement through the spot market to realize short-term power supply and demand balance. In the electricity spot market, with the increase of trading varieties, frequency increase, and more frequent price fluctuations, and under the new rules of "real-time market de...

AI Technical Summary

Problems solved by technology

[0004] For the above-mentioned algorithm used to predict the short-term power load of industrial enterprises, the feature processing process in the forecasting process is complicated, and the difference between the forecast result and the actual power load is large, which cannot provide a reference for the power spot market transaction

Images