A Novel Probabilistic Electricity Price Forecasting Framework with Multi-Factorial Cross-Correlation Embedding and Patch Processing

Accurate probabilistic electricity price forecasting can characterize the uncertainty of electricity price information and provide more comprehensive forecasting information for market traders. However, existing methods struggle to effectively identify and extract factors related to electricity prices, resulting in limited prediction accuracy. To this end, we propose a novel probabilistic electricity price forecasting framework. Specifically, the framework can be divided into two parts. In the two-stage data preprocessing module, we utilize the Isolation Forest algorithm for outlier correction and ReliefF for feature selection to reduce feature redundancy. In the probabilistic electricity price forecasting module, we utilize the Multilayer Perceptron (MLP) to learn trend features from historical price data, while the customized feature convolutional network (FConv) extracts the feature patterns of factors related to electricity prices. Then, the cross-correlation embedding technique fuses these extracted features for a more comprehensive representation. In addition, the patch processing technique partitions the fused data into multiple patches to capture local features. Moreover, the incorporation of positional encoding helps the model to better capture temporal dependencies in the data. Then, we utilize the MLP to generate the final forecasts. Additionally, the proposed framework is trained using an improved loss function with penalty weights, aiming to enhance the model’s capability to predict outliers. Finally, we validated the effectiveness of our proposed model using two electricity price datasets from China.