Short-term power load forecasting based on combined kernel Gaussian process hybrid model

Liang Lingyu,Zhaojie Dong,Xinde Zhu,Wenqi Huang,Peng Li,Jiguang Zhao,Bingfang Lu,P Siano,Q Li

doi:10.1051/e3sconf/202125601009

Abstract

As one of the countries with the most energy consumption in the world, electricity accounts for a large proportion of the energy supply in our country. According to the national basic policy of energy conservation and emission reduction, it is urgent to realize the intelligent distribution and management of electricity by prediction. Due to the complex nature of electricity load sequences, the traditional model predicts poor results. As a kernel-based machine learning model, Gaussian Process Mixing (GPM) has high predictive accuracy, can multi-modal prediction and output confidence intervals. However, the traditional GPM often uses a single kernel function, and the prediction effect is not optimal. Therefore, this paper will combine a variety of existing kernel to build a new kernel, and use it for load sequence prediction. In the electricity load prediction experiments, the prediction characteristics of the load sequences are first analyzed, and then the prediction is made based on the optimal hybrid kernel function constructed by GPM and compared with the traditional prediction model. The results show that the GPM based on the hybrid kernel is not only superior to the single kernel GPM but also superior to some traditional prediction models such as ridge regression, kernel regression and GP.

Highlights

As one of the countries with the most energy consumption in the world, electricity accounts for a large proportion of the energy supply in our country
After continuous exploration, domestic and foreign scholars have proposed a series of effective load intelligent forecasting methods, such as time series method, artificial neural network (ANN) and support vector machine (SVM) forecasting
The test samples are assigned to the first group, and the prediction distribution can be obtained from the prediction formula of the single Gaussian Process (GP) component

Summary

2Principle of Gaussian Process Mixture Model

Learning algorithm of Gaussian process mixture model This paper adopts the iterative learning algorithm of hidden variable posterior hard partition proposed by Chen [6]. In step E, the learning samples are allocated according to the maximum posterior probability criterion, and each step is estimated by the maximum likelihood method in step M. The specific implementation steps of the algorithm are as follows: The first step: For a given learning sample, divide it into several groups by k-means clustering algorithm; Step 2: Independent learning of each GP component participating in the mixing based on maximum likelihood estimation; The third step: According to the maximum posterior probability criterion, re-designate the group of the learning sample. Step 4: After the learning process is over, for a given test sample, if the corresponding target output is predicted, the group can be specified according to the maximum posterior probability criterion. The required learning sample in this predictive formula is the learning sample assigned to the group in the last iteration

3Prediction Algorithm of Combined Kernel Gaussian Process Mixture Model

Predicted value