Intraday electricity markets (IDM), which is designed to correct forecast error of renewable energy generations and enable energy trading, are characterized by high volatility and rapid price fluctuations, which not only provide market participants with strong motivation to make accurate price predictions, but also present significant challenges. The use of machine learning methods for price prediction has become a major trend in recent research. However, in previous studies, only a few specific features, such as Volume-Weighted Average Price (VWAP) and last transaction price \cite{abstract167, lasso39, abstract168}, have been applied, while the rich features embedded in orderbooks have not received sufficient attention. Furthermore, while quantile regression tasks, which provide richer information for trading strategies, have been employed in IDM price prediction, they have generally been confined to statistical models \cite{sta131, lasso38}. Deep learning-based quantile regression, capable of capturing nonlinear relationships and incorporating uncertainty, has yet to be applied. Additionally, in current research, IDM price prediction is often based on a specific orderbook, and thus, the generalization of prediction methods across different orderbooks, as well as their structural similarities across different markets and product types, has not been convincingly addressed.

To address the challenges mentioned above, this report focuses on the German and Austrian markets over continuous trading periods from January 2022 to January 2025, considering both hourly and quarter-hourly products. A total of 384 feature candidates were extracted, including percentiles, momentum, and volatility of prices and trading volumes on both buy and sell sides across multiple time windows. For the extracted feature candidates, we propose an innovative feature selection approach based on their correlation with normal and extreme price labels. Comparative experiments demonstrate that this algorithm outperforms L1-based selection and Principal Component Analysis (PCA) compression in quantile forecast evaluations. Based on the selected features, Quantile LightGBM (QLGBM), Quantile Extreme Gradient Boosting (QXGB), Quantile Multilayer Perceptron (QMLP), and Quantile Kolmogorov–Arnold Network (QKAN) were used to predict the labels, providing a comprehensive set of benchmarks. Additionally, generalization studies across markets and products were conducted using transfer learning, with multiple strategies such as zero-shot, fine-tuning, and joint learning applied. The results reveal valuable insights into the relationships between different markets and product types.