AI2030 Responsible AI Tools Marketplace

Key Takeaways

Deep neural networks can outperform traditional economic models for electricity price forecasting, but their superior predictive power comes at the cost of transparency—this paper demonstrates that explainable AI (XAI) methods can help bridge that gap by revealing which factors actually drive price changes in complex electricity markets.
SHAP and gradient-based visualization techniques successfully identified the key features influencing electricity prices across five different markets, suggesting that XAI tools can make black-box AI models interpretable enough for use in critical infrastructure decisions where understanding "why" matters as much as accuracy.
The paper introduces new XAI visualization methods (SSHAP values and SSHAP lines) specifically designed to handle high-dimensional data in electricity markets, indicating that generic explainability tools may need customization for domain-specific applications to be truly useful for stakeholders.
Using XAI to understand electricity market dynamics has practical value beyond just model validation—it can help policymakers, regulators, and market participants better understand how their markets actually function, potentially informing better policy decisions in energy systems.
This work exemplifies responsible AI in practice: rather than deploying a powerful but opaque model, the researchers prioritized interpretability alongside performance, demonstrating that stakeholder trust and understanding should be built into AI systems from the start, especially in sectors affecting public welfare.

Abstract

Electricity markets are highly complex, involving lots of interactions and complex dependencies that make it hard to understand the inner workings of the market and what is driving prices. Econometric methods have been developed for this, white-box models, however, they are not as powerful as deep neural network models (DNN). In this paper, we use a DNN to forecast the price and then use XAI methods to understand the factors driving the price dynamics in the market. The objective is to increase our understanding of how different electricity markets work. To do that, we apply explainable methods such as SHAP and Gradient, combined with visual techniques like heatmaps (saliency maps) to analyse the behaviour and contributions of various features across five electricity markets. We introduce the novel concepts of SSHAP values and SSHAP lines to enhance the complex representation of high-dimensional tabular models.

Key Takeaways

Deep neural networks can outperform traditional economic models for electricity price forecasting, but their superior predictive power comes at the cost of transparency—this paper demonstrates that explainable AI (XAI) methods can help bridge that gap by revealing which factors actually drive price changes in complex electricity markets.
SHAP and gradient-based visualization techniques successfully identified the key features influencing electricity prices across five different markets, suggesting that XAI tools can make black-box AI models interpretable enough for use in critical infrastructure decisions where understanding "why" matters as much as accuracy.
The paper introduces new XAI visualization methods (SSHAP values and SSHAP lines) specifically designed to handle high-dimensional data in electricity markets, indicating that generic explainability tools may need customization for domain-specific applications to be truly useful for stakeholders.
Using XAI to understand electricity market dynamics has practical value beyond just model validation—it can help policymakers, regulators, and market participants better understand how their markets actually function, potentially informing better policy decisions in energy systems.
This work exemplifies responsible AI in practice: rather than deploying a powerful but opaque model, the researchers prioritized interpretability alongside performance, demonstrating that stakeholder trust and understanding should be built into AI systems from the start, especially in sectors affecting public welfare.

Abstract

Explaining deep neural network models for electricity price forecasting with XAI

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

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Explaining deep neural network models for electricity price forecasting with XAI

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links