Multivariate Scenario Generation of Day-Ahead Electricity Prices using Normalizing Flows

Core Concepts

Probabilistic forecasting of day-ahead electricity prices using normalizing flows yields accurate and high-quality scenarios.

Abstract

Introduction:

Accurate information on day-ahead electricity prices is crucial for trading.
Deriving forecasting models is challenging due to non-stationarity.

Modeling Approach:

Normalizing flow generates full-day scenarios based on conditional features.
Adaptive retraining allows the model to adapt to changing market conditions.

Results:

Model reproduces true price distribution and yields accurate forecasts.
Scenarios reflect correlations present in actual price time series.

Performance Evaluation:

Normalizing flow outperforms benchmark models in terms of MAE, ES, and VS scores.
Uncertainty estimation correlates with forecast errors, indicating trustworthiness of predictions.

Limitations and Future Directions:

Mismatch in representing regulatory aspects leads to smoothed increment distributions.
Inclusion of more recent data may improve model performance.

Stats

Our modeling approach generates full-day scenarios of day-ahead electricity prices based on conditional features such as residual load forecasts.
Our results highlight that the normalizing flow generates high-quality scenarios that reproduce the true price distribution and yield accurate forecasts.
The model inherits price, demand, and renewable power generation data from the previous day as conditional inputs.
We evaluate the model performance and provide a detailed statistical analysis, comparing predictions and the actual price time series.
The results show that the model reproduces the intricate statistical properties of the price time series, including heavy-tailed distribution as well as conditional distributions, temporal correlations, and the impact of changing market conditions.

Quotes

"We present a probabilistic forecasting approach for day-ahead electricity prices using the fully data-driven deep generative model called normalizing flow."
"Our modeling approach generates full-day scenarios of day-ahead electricity prices based on conditional features such as residual load forecasts."
"Our results highlight that the normalizing flow generates high-quality scenarios that reproduce the true price distribution and yield accurate forecasts."
"Our previous work [12] is the only work using normalizing flows to predict electricity prices."
"The normalizing flow adapts to changing market conditions and adjusts its estimate of uncertainty for forecasts."

Key Insights Distilled From

Multivariate Scenario Generation of Day-Ahead Electricity Prices using Normalizing Flows

by Hannes Hilge... at arxiv.org 03-25-2024

https://arxiv.org/pdf/2311.14033.pdf

Deeper Inquiries

How can regulatory aspects affecting energy markets be better incorporated into scenario generation models

Incorporating regulatory aspects affecting energy markets into scenario generation models can be achieved through several strategies. One approach is to include specific rules and constraints in the model that reflect the regulatory framework governing the energy market. For example, if regulations prohibit negative electricity prices or impose certain price caps, these constraints can be integrated into the modeling process to ensure that generated scenarios adhere to regulatory requirements.
Another strategy is to incorporate external data sources related to regulatory changes or announcements. By monitoring and integrating information on policy updates, market regulations, or government interventions that impact energy markets, scenario generation models can adjust their forecasts accordingly. This proactive approach allows the model to adapt quickly to changing regulatory environments and produce more accurate scenarios.
Furthermore, collaborating with domain experts such as policymakers, regulators, or industry professionals can provide valuable insights into upcoming regulatory changes and their potential effects on energy markets. By leveraging expert knowledge and incorporating it into the modeling process, scenario generation models can better capture the nuances of regulatory impacts on market dynamics.
Overall, by actively considering and integrating regulatory aspects into scenario generation models through constraints, external data sources, and expert input, these models can offer more realistic and reliable forecasts for decision-making in energy markets.

What are potential strategies to address discrepancies in representing negative electricity prices in scenario forecasts

Discrepancies in representing negative electricity prices in scenario forecasts can be addressed through various strategies aimed at improving model performance:

Data Preprocessing: Implementing preprocessing techniques specifically designed to handle extreme values like negative prices can help mitigate discrepancies. This may involve adjusting scaling factors or applying transformations to normalize data distributions effectively.

Model Calibration: Fine-tuning model parameters or hyperparameters based on historical data patterns related to negative prices could improve forecasting accuracy for such scenarios.

Scenario Generation Techniques: Exploring alternative generative modeling approaches that are better suited for capturing rare events like negative prices might enhance representation accuracy in scenarios.

Regulatory Considerations: Incorporating explicit rules within the model architecture regarding restrictions on negative pricing (as per market regulations) could help align forecasted scenarios with real-world constraints.

Continuous Learning: Implementing adaptive learning mechanisms that allow the model to update itself dynamically based on new data inputs could enable better adaptation towards evolving market conditions involving negative price occurrences.

How might advancements in machine learning further enhance multivariate probabilistic forecasting models for energy markets

Advancements in machine learning offer significant opportunities for enhancing multivariate probabilistic forecasting models for energy markets:

Deep Learning Architectures: Leveraging advanced neural network architectures such as transformer-based models or graph neural networks tailored for time series analysis could improve forecasting accuracy by capturing complex dependencies among multiple variables simultaneously.

Uncertainty Quantification Techniques: Integrating state-of-the-art uncertainty quantification methods like Bayesian deep learning or ensemble methods enables more robust estimation of prediction intervals and probabilistic forecasts.

Interpretability Tools: Developing interpretable machine learning techniques like SHAP values or attention mechanisms helps understand feature importance and underlying relationships driving forecast outcomes.

4 .Real-time Data Integration: Utilizing streaming algorithms combined with online learning capabilities facilitates continuous updating of models with incoming data streams from sensors/devices connected within smart grids.
5 .Hybrid Models: Combining traditional statistical approaches with modern machine learning algorithms creates hybrid forecasting frameworks capable of handling diverse datasets efficiently while ensuring high predictive performance.
These advancements hold promise for enhancing the accuracy,
reliability,
and interpretability
of multivariate probabilistic forecasting
models
for
energy
markets,
supporting informed decision-making processes across various stakeholders involved in this sector.