Short-term electric power prediction in a certain area based on least squares support vector machine

Abstract

Short-term power prediction is one of the important tasks in the daily operation of the energy system. With the popularity of smart grid, it brings more difficulties to the accuracy of electricity prediction. The purpose of this study is to explore the characteristics of short-term power data to achieve efficient and accurate power prediction and provide a reference for power grid operation management. Firstly, according to various characteristics of power prediction, this paper builds shortterm power prediction models by using SVM and LS-SVM, respectively. Then, this paper compares the prediction results of the two single models with the test values. It is found that the prediction curve of the LS-SVM model is better than that of the SVM model, and the LS-SVM prediction model has better potential for prediction short-term power consumption. This indicates that different machine learning models are suitable for different types of electrical power predictions.