Electricity Price Prediction Using Multikernel Gaussian Process Regression Combined With Kernel-Based Support Vector Regression

This paper presents a new hybrid model for predicting German electricity prices. The algorithm is based on a combination of Gaussian process regression (GPR) and support vector regression (SVR). Although GPR is a competent model for learning sto-chastic patterns within data and for interpolation, its performance for out-of-sample data is not very promising. By choosing a suitable data-dependent covariance function, we enhance the performance of GPR. However, since the out-of-sample prediction is dependent on the training data, the prediction is vulnerable to noise and outliers. To overcome this issue, a separate prediction is calculated using SVR, which applies margin-based optimization. This method is advantageous when dealing with nonlinear processes and outliers, since only certain necessary points (support vectors) in the training data are responsible for regression. The individual predictions are then linearly combined using uniform weights. We evaluate the method on historical German day-ahead prices (2021–2023) and show that it outperforms publicly available benchmarks, namely, the LASSO estimated autore-gressive regression model and the deep neural network benchmark from the recent literature.