Variational mode decomposition-based short-term power load prediction method and system

Abstract

The invention particularly relates to a variational mode decomposition-based short-term power load prediction method. The method comprises the following steps of S1, obtaining load data and multivariate related data of a prediction day and three months before the prediction day; S2, preprocessing and associating data; S3, carrying out power load sequence modal decomposition; S4, judging the temperature correlation; S5, generating a feature vector of each component; S6, establishing an adaptive step length load prediction model; S7, establishing a power load prediction model by using an LGBM gradient boosting algorithm; S8, integrating prediction results; and S9, correcting a prediction result. The invention also comprises a power load prediction system which comprises a data acquisition module, a data preprocessing and correlation module, a load sequence modal decomposition module and a temperature correlation discrimination module to generate characteristic vectors of each component,a load fluctuation discrimination and model adjustment module, a prediction module of each component, and a prediction result integration module of each component and a prediction result correction module. The invention is suitable for the complex composition condition of each component of the power load, and is high in prediction precision, more flexible to use and good in universality.

Description

technical field [0001] The invention relates to the field of electric power technology, in particular to a short-term power load forecasting method and system based on variational mode decomposition. Background technique [0002] Power load forecasting plays an important role in the dispatching and operation of power system. Studies have shown that for every 1% increase in the short-term load forecast error, the UK's annual grid cost will increase by about 17.7 million pounds; while in Norway, every 1% increase in the short-term load forecast error will result in an additional operation of 4.5-9 million euros cost. Therefore, accurate short-term load forecasting is helpful to discover the critical state of the system, formulate a reasonable transfer power supply plan, improve the management level of the power demand side, and reduce equipment overload. construction is of great importance. [0003] Power system load data is a typical non-stationary time series with periodi...

AI Technical Summary

Problems solved by technology

However, the above-mentioned patents all have the following disadvantages: First, although the patent uses modal decomposition, they use empirical modal decomposition, which has poor effect and relatively poor stability

Secondly, it does not consider the influence of load information and environmental factors such as holidays and temperature.

Third, the sudden change in load is not considered, and the sudden change in load cannot be effectively dealt with, resulting in a sharp drop in accuracy; fourth, the model result is directly used as the final prediction result, and the accuracy is relatively low

Based on the analysis of the existing technology based on the actual demand and data base of short-term load forecasting, it is found that there are mainly the following deficiencies: (1) The existing technology cannot cope well with the situation of sudden load changes caused by power transfer, temperature mutation, etc. Forecasting; (2) The load fluctuation in a steady state has a certain periodicity, but this periodicity will be broken in a non-stationary state. Only relying on this periodic law cannot sense the load fluctuation state in time, which leads to a certain degree of prediction Hysteresis; (3) The power load can be regarded as a superposition of a series of complex basic unit power consumption data

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