Explainable AI (XAI) in Energy

Topic Information

Dear Colleagues,

The integration of Artificial Intelligence (AI) has revolutionized energy systems, providing unprecedented capabilities in demand forecasting, price prediction, and grid optimization. However, the “black-box” nature of many high-performance models remains a significant barrier to their widespread adoption by policymakers and industry stakeholders who require transparency and accountability.

This Topic, titled “Explainable AI (XAI) in Energy,” focuses on emerging methodologies that bridge the gap between predictive power and human interpretability within Energy Economics. We invite original research and review articles that explore the application of any XAI techniques (SHAP, LIME, counterfactual explanations, etc.) and ante hoc interpretable models to address critical challenges in energy markets.

As Artificial Intelligence becomes the backbone of modern energy systems, the “black-box” nature of advanced algorithms poses significant challenges for trust, safety, and regulatory compliance. This Topic, “Explainable AI (XAI) in Energy,” aims to bridge the gap between high-performance machine learning and human interpretability. We invite submissions that explore emerging methodologies in analysis, modeling, and forecasting within Energy Economics. We seek empirical work that utilizes XAI to provide transparent insights into complex energy phenomena, ranging from market volatility to the stability of renewable-heavy grids. By prioritizing “interpretable-by-design” models and post hoc explanation techniques, this Topic seeks to empower stakeholders, policymakers, and engineers with actionable, robust, and ethical AI solutions.

We particularly seek submissions focusing on the following:

• Energy Market Intelligence: XAI applications in the spot and derivatives markets, algorithmic trading, price formation, and financial risk management.
• Grid Operations and Infrastructure: Interpretable models for load and demand forecasting, smart grid stability, and predictive maintenance of energy assets.
• Renewable Energy and Sustainability: Transparent modeling of wind, solar, and hydro production, as well as CO₂ emission tracking and carbon credit trading.
• Policy, Ethics, and Social Welfare: Using XAI to inform energy taxation, mitigate energy poverty, and ensure fairness in energy distribution and pricing.
• Methodological Innovations: Development of ante hoc (inherently interpretable) or post hoc (SHAP, LIME, etc.) methods specifically tailored for the non-linear dynamics of energy data.
• Storage and Logistics: XAI-driven insights into energy storage systems (ESSs), hydrocarbon logistics, and supply chain optimization.

Papers should emphasize theoretical robustness, methodological innovation, and the practical applicability of their results for a sustainable energy future, factors that are the basic requirements for a paper to be considered for publication.

Prof. Dr. Periklis Gogas
Prof. Dr. Theophilos Papadimitriou
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	6.5	7.3	2020	20.4 Days	CHF 1800	Submit
Clean Technologies cleantechnol	5.9	9.4	2019	20.9 Days	CHF 1800	Submit
Energies energies	3.9	8.3	2008	16.7 Days	CHF 2600	Submit
Machine Learning and Knowledge Extraction make	8.4	12.7	2019	18.7 Days	CHF 1800	Submit
Sci sci	4.1	5.4	2019	28.2 Days	CHF 1400	Submit
Sustainability sustainability	4.1	8.9	2009	16.9 Days	CHF 2400	Submit

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