Regime-aware conditional neural processes with multi-criteria decision support for operational electricity price forecasting
将贝叶斯机制检测与条件神经过程结合，预测德国、法国和挪威市场未来24小时电价，并通过TOPSIS多准则决策评估模型在运营优化中的表现。
This work integrates Bayesian regime detection with conditional neural processes for 24-hour electricity price forecasting in the German, French, and Norwegian markets. Regimes are inferred via a disentangled sticky hierarchical Dirichlet process hidden Markov model (DS-HDP-HMM). For each regime, an independent conditional neural process (CNP) learns localized mappings from input contexts to 24-dimensional hourly price trajectories; final forecasts are produced as regime-weighted mixtures of the regime-specific CNP outputs. Temporal robustness and cross-market generalization are evaluated on Germany (2021–2023) and on France and Norway (2023). We benchmark against deep neural networks (DNN), the Lasso estimated autoregressive (LEAR) model, extreme gradient boosting (XGBoost), Bayesian long short-term memory (BLSTM), and the temporal fusion transformer (TFT), and assess downstream value through battery storage optimization. Results indicate that the proposed regime-aware CNP often delivers higher profits or lower costs, while DNN can be exceptionally competitive in specific cost-minimization settings. Because point accuracy does not necessarily translate into operational optimality, we apply the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to aggregate forecasting and operational criteria. TOPSIS ranks the CNP as the leading model for 2023 and, overall, as the most balanced and consistently preferred solution across the considered markets.