Electricity price forecasting across Norway's five bidding zones in the post-crisis era

TL;DR

This study evaluates various electricity price forecasting models across Norway's five bidding zones, emphasizing the importance of external features and regime analysis for accurate predictions amid market changes.

Contribution

It provides a comprehensive benchmark of forecasting models across all Norwegian zones, highlighting the effectiveness of LightGBM and the significance of regime-aware features.

Findings

LightGBM outperforms other models in all zones with MAE 1.64-5.74 EUR/MWh.

Lagged prices and calendar variables alone often match full multimodal models.

Forecast errors increase under stressed market regimes, emphasizing the need for regime-aware models.

Abstract

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.