Norway's electricity market is heavily dominated by hydropower, but the 2021--2022 energy crisis and stronger integration with Continental Europe have fundamentally altered price formation, reducing the reliability of forecasting models calibrated on historical data. Despite the critical need for updated models, a unified benchmark evaluating feature contributions across all structurally diverse Norwegian bidding zones remains lacking. Here we present a comprehensive evaluation of electricity price forecasting across all five Norwegian Nord Pool bidding zones. We constructed a multimodal hourly dataset spanning 2019--2025 and evaluated eight forecasting model families including LightGBM, ARX, and advanced deep learning architectures using a strictly causal test set. We implemented robust rolling-origin backtesting, leave-one-group-out feature ablation, and conditional regime analysis to dissect model performance and feature utility. Our results show that LightGBM achieves the best performance in every zone with MAE ranging from 1.64 to 5.74~EUR/MWh, while the ridge ARX model remains a highly competitive linear benchmark in northern zones. Feature ablation reveals that models relying solely on lagged prices and calendar variables achieve high accuracy and often match or exceed full multimodal integration. However, conditional regime analysis demonstrates that external features like reservoir levels and gas prices remain crucial to stratify forecast errors, which consistently increase under stressed market regimes. This highlights the practical value of model interpretability and regime awareness for decision makers facing structural changes in market dynamics.

翻译：挪威电力市场以水电为主导，但2021—2022年能源危机及与欧洲大陆更深度的整合从根本上改变了价格形成机制，降低了基于历史数据校准的预测模型的可靠性。尽管亟需更新模型，但目前仍缺乏一个统一的基准来评估所有结构多样的挪威竞价区中特征贡献。本文对挪威所有五个Nord Pool竞价区的电价预测进行了全面评估。我们构建了覆盖2019—2025年的多模态小时级数据集，并采用严格因果测试集，评估了包括LightGBM、ARX及先进深度学习架构在内的八个预测模型系列。我们实施了稳健的滚动原点回溯检验、留一组法特征消融及条件机制分析，以剖析模型性能与特征效用。结果表明，LightGBM在每个竞价区均表现最佳，平均绝对误差（MAE）范围为1.64至5.74欧元/兆瓦时，而岭回归ARX模型在北部竞价区仍为极具竞争力的线性基准。特征消融显示，仅依赖滞后价格与日历变量的模型即可实现高精度，且常能匹配或超越完整多模态融合的效果。然而，条件机制分析表明，水库水位与天然气价格等外部特征对区分预测误差仍至关重要，这些误差在市场承压机制下持续增大。这凸显了模型可解释性与机制意识对面临市场动态结构性变化的决策者的实际价值。