Bus load prediction method

Abstract

The invention discloses a bus load prediction method. The method comprises the following steps: correcting abnormal values in historical load data by use of a transverse comparison method, and determining key influence factors of bus load by use of a grey relation projection method; putting load curves with similar features in the same category by use of an improved K-Means clustering method to get a plurality of typical load patterns, building a random forest classification model, and establishing the mapping relationship between influence factors and clustering results; for each load pattern, training a plurality of prediction models by use of a multivariate linear regression method; and determining the category of a day under test, and selecting a matching regression model to realize load prediction. A data mining method is introduced to analyze the changing rules of bus load, and a prediction model library is built. Model matching is realized based on the category of a day under test. The accuracy and real-time performance of short-term bus load prediction are improved. More accurate decision support is provided for power grid planning and real-time dispatching.

Description

technical field [0001] The invention relates to the technical field of power system engineering, in particular to a bus load forecasting method. Background technique [0002] Bus load forecasting is an important part of power system planning and the basis of power system economic operation. Its forecast results can better realize distributed load management and directly affect the analysis results of subsequent safety checks of the power grid. , reactive power optimization, local control of the plant and station, and reduction of power generation costs are of great significance. As the off-grid load of the substation, the bus load has a small base, weak stability, no obvious change trend, and many high-frequency fluctuation components, which have become difficulties in improving the prediction accuracy. Short-term bus load forecasting generally refers to real-time forecasting, which not only requires high forecasting accuracy, but also fast calculation speed. Due to the cha...

AI Technical Summary

Problems solved by technology

When dealing with the problem of bus load forecasting considering multiple influencing factors, although the regression analysis method is simple to implement, it has high requirements for the stability of the sample, and it is not suitable to directly deal with the bus load with strong fluctuations.

Neural network and support vector regression methods have strong nonlinear fitting ability and do not require high sample stability. However, when the input data has too many dimensions and the sample size is large, the training of the model tends to fall into local optimum. , and the complexity is high, thus reducing the prediction accuracy and computational efficiency

Moreover, with the rise of big data technology and the rapid development of sensing technology, power big data has been formed, and the internal characteristics of the bus load are more complex, which is not conducive to the establishment of prediction models.

Therefore, the existing bus load forecasting algorithm cannot achieve a satisfactory forecasting effect

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