In this paper, we develop a new approach to the very short-term point forecasting of electricity prices in the continuous market. It is based on the Support Vector Regression with a kernel correction built on additional forecast of dependent variable. We test the proposed approach on a dataset from the German intraday continuous market and compare its forecast accuracy with several benchmarks: classic SVR, the LASSO model, Random Forest and the na\"{i}ve forecast. The analysis is performed for different forecasting horizons, deliveries, and lead times. We train the models on three expert sets of explanatory variables and apply the forecast averaging schemes. Overall, the proposed cSVR approach with the averaging scheme yields the highest forecast accuracy, being at the same time the fastest from the considered benchmarks. The highest improvement in forecast accuracy is obtained for deliveries in the morning and evening peaks.

翻译：本文针对连续市场中电价的超短期点预测提出了一种新方法。该方法基于支持向量回归，并通过对因变量的附加预测构建核函数修正。我们在德国日内连续市场数据集上测试了所提方法，并将其预测精度与多个基准模型进行比较：经典支持向量回归、LASSO模型、随机森林以及朴素预测。分析针对不同预测时间范围、交割时段和前置时间进行。我们使用三组专家解释变量训练模型，并应用预测平均方案。总体而言，所提出的cSVR方法结合平均方案获得了最高的预测精度，同时是所考虑基准模型中速度最快的。预测精度提升最显著体现在早晚高峰时段的交割预测中。