Short period load prediction method based on kernel principle component analysis and random forest

Abstract

The invention discloses a short period load prediction method based on kernel principle component analysis and a random forest. The a short period load prediction method comprises the following steps of: (1) analyzing and selecting data influencing load prediction precision of a day to be predicted in an operational electric power system, and preliminarily constructing training and prediction sample sets; (2) utilizing kernel principle component analysis to carry out dimensionality reduction on training sample data; (3) utilizing a random forest model to train the training sample data after the dimensionality reduction, and obtaining the random forest model after the training; and (4) inputting prediction sample data into the random forest model after the training, and carrying out short period load prediction of the day to be predicted. The short period load prediction method has the advantages that the kernel principle component analysis and the random forest model are combined for carrying out short period load prediction on the electric power system, the prediction precision, efficiency and data rationality are improved.

Description

technical field [0001] The invention belongs to the technical field of power systems, and in particular relates to a short-term load forecasting method based on kernel principal component analysis and random forest. Background technique [0002] Power load forecasting is one of the important tasks of power system dispatching, power consumption, planning, planning and other management departments. Accurate short-term load forecasting is an important basis for rationally arranging the start and stop of generating units, improving power quality, and maintaining safe and stable operation of the power grid. Improve economic and social benefits. [0003] In order to improve the accuracy of short-term load forecasting, many researchers have carried out long-term research and exploration, and formed the traditional forecasting method represented by time series method and the artificial intelligence method represented by artificial neural network (Artificial Neural Networks, ANN). ...

AI Technical Summary

Problems solved by technology

[0004] However, there are generally two problems in the actual application process: the first problem is that when there are too many input factors, the structure of the prediction model will be too complex and the training efficiency will be low; the second problem is that the ANN method is easy to cause The problem of under-learning or over-fitting

Although machine learning algorithms such as SVM can effectively avoid the risk of falling into a local minimum and achieve more accurate predictions, they still have the following deficiencies: (1) The kernel function must meet the Mercer condition, and there are few optional kernel functions; (2) To achieve point prediction, it is impossible to describe the uncertain information of the data; (3) There are many parameters, and the support vector increases linearly with the increase of training samples, and the amount of calculation is large

In the early days, the RF algorithm was mostly used in classification problems such as image recognition and fault diagnosis. At present, there are very few achievements in applying this algorithm to load forecasting.

Images