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On-line Conformalized Neural Network Ensembles for Probabilistic Forecasting of Day-Ahead Electricity Prices


Core Concepts
A novel approach to probabilistic electricity price forecasting that extends state-of-the-art neural network ensembles through conformal inference techniques, deployed within an on-line recalibration procedure, to achieve improved hourly coverage and stable probabilistic scores.
Abstract
The content discusses the problem of probabilistic electricity price forecasting (PEPF), which is of increasing interest due to the demand for proper quantification of prediction uncertainty to support operations in complex power markets with increasing renewable generation. The key highlights and insights are: Distributional neural network ensembles have been shown to outperform state-of-the-art PEPF benchmarks, but they lack the required calibration capabilities, failing to pass coverage tests at various prediction horizons. The authors propose a novel approach to PEPF that extends the deep ensemble-based methods through conformal inference techniques, deployed within an on-line recalibration procedure. The developed method leverages asymmetric conformal prediction for regression tasks, enabling flexible step-wise compensations of the upper/lower prediction bands beyond the marginal coverage. It also integrates adversarial conformal inference to address the lack of robustness of conformal prediction under non-exchangeable conditions, such as distribution shifts. The authors explore both quantile regression and distributional neural networks to estimate the conditional quantiles to be calibrated, and employ a uniform vincentization technique for ensemble aggregation. Experiments are conducted on multiple market regions, including the German and Italian day-ahead markets, achieving improved hourly coverage and stable probabilistic scores compared to state-of-the-art benchmarks.
Stats
The content does not provide specific numerical data or metrics, but rather focuses on the methodological development and experimental evaluation of the proposed probabilistic electricity price forecasting approach.
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Deeper Inquiries

What are the potential implications of the proposed approach for real-world power market operations and decision-making processes

The proposed approach of using conformal inference-based techniques for probabilistic electricity price forecasting (PEPF) has significant implications for real-world power market operations and decision-making processes. By incorporating conformal prediction methods into neural network ensembles for PEPF, the reliability and accuracy of forecasting models can be enhanced. This improvement in forecasting accuracy can directly impact the decision-making processes of utilities, retailers, aggregators, and large consumers in liberalized energy markets. One key implication is the ability to provide more accurate and reliable forecasts of day-ahead electricity prices, which are crucial for bidding strategies, optimal generation and asset management, energy-aware planning, and scheduling. With the increasing share of renewable energy sources in the generation mix and the complex dynamics of power markets, having probabilistic forecasts with quantified prediction uncertainty is essential for risk optimization, stochastic optimization, and scenario analysis before trading. The use of conformal inference-based techniques can also lead to improved operational efficiency and cost savings for companies operating in power markets. By providing sharper and more calibrated probabilistic forecasts, decision-makers can make more informed and strategic decisions, leading to better market participation and overall performance in the energy sector.

How could the performance of the conformal inference-based PEPF method be further improved, for example, by exploring alternative neural network architectures or ensemble combination techniques

To further improve the performance of the conformal inference-based PEPF method, several strategies can be explored: Exploration of Alternative Neural Network Architectures: Investigating different neural network architectures, such as recurrent neural networks, transformers, or attention mechanisms, can help capture complex temporal dependencies and patterns in electricity price time series data. These architectures may offer better modeling capabilities for capturing the dynamics of power markets and improving forecasting accuracy. Ensemble Combination Techniques: Experimenting with alternative ensemble combination techniques, such as weighted averaging, stacking, or boosting, can enhance the diversity and robustness of the ensemble models. By combining predictions from multiple models in a more sophisticated manner, the overall forecasting performance can be improved. Hyperparameter Tuning: Fine-tuning the hyperparameters of the neural network models and the conformal inference framework can optimize the model's performance. Grid search, Bayesian optimization, or automated hyperparameter tuning techniques can help identify the best set of hyperparameters for the PEPF method. Feature Engineering: Exploring additional exogenous variables, lagged features, or engineered features that capture relevant market dynamics can enhance the model's predictive capabilities. Feature selection techniques and domain knowledge can guide the selection of informative features for the forecasting models. Model Interpretability: Enhancing the interpretability of the models can provide valuable insights into the factors driving electricity price movements. Techniques such as SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) can help explain the model's predictions and improve trust in the forecasting results.

What are the potential connections between the challenges faced in probabilistic electricity price forecasting and those encountered in other time series forecasting domains, and how could insights from those domains inform future developments in PEPF

The challenges faced in probabilistic electricity price forecasting (PEPF) share similarities with those encountered in other time series forecasting domains, such as financial forecasting, weather forecasting, and demand forecasting. Insights from these domains can inform future developments in PEPF in the following ways: Modeling Volatility: Just like in financial forecasting, electricity prices exhibit volatility and complex patterns that require sophisticated modeling techniques. Methods used in financial forecasting to model volatility, such as GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, can be adapted for PEPF to capture price fluctuations and uncertainty. Incorporating Exogenous Factors: Weather forecasting techniques, which consider external factors like temperature, precipitation, and wind speed, can inspire the inclusion of exogenous variables in PEPF models. By incorporating weather data and other external factors, PEPF models can better capture the impact of external variables on electricity prices. Ensemble Methods: Ensemble methods, widely used in weather forecasting to combine multiple models for improved accuracy, can be applied to PEPF. Techniques like model averaging, stacking, and boosting can enhance the robustness and reliability of electricity price forecasts by leveraging the diversity of different models. Uncertainty Quantification: Similar to demand forecasting, where uncertainty quantification is crucial for decision-making, PEPF requires accurate quantification of prediction uncertainty. Bayesian methods, quantile regression, and distributional forecasting techniques used in demand forecasting can be adapted for PEPF to provide probabilistic forecasts with quantified uncertainty. By drawing insights and methodologies from these related domains, PEPF can benefit from advancements in modeling techniques, data preprocessing, and model evaluation, leading to more accurate and reliable electricity price forecasts.
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