AI Bias in Power Systems Domain—Exemplary Cases and Approaches

Chijioke Eze, Abraham Ezema, Lara Roth, Zhiyu Pan, Ferdinanda Ponci () and Antonello Monti
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Chijioke Eze: Institute for Automation of Complex Power Systems, RWTH Aachen University, Mathieu Strasse 10, 52074 Aachen, North Rhine-Westphalia, Germany
Abraham Ezema: Institute for Automation of Complex Power Systems, RWTH Aachen University, Mathieu Strasse 10, 52074 Aachen, North Rhine-Westphalia, Germany
Lara Roth: Institute for Automation of Complex Power Systems, RWTH Aachen University, Mathieu Strasse 10, 52074 Aachen, North Rhine-Westphalia, Germany
Zhiyu Pan: Institute for Automation of Complex Power Systems, RWTH Aachen University, Mathieu Strasse 10, 52074 Aachen, North Rhine-Westphalia, Germany
Ferdinanda Ponci: Institute for Automation of Complex Power Systems, RWTH Aachen University, Mathieu Strasse 10, 52074 Aachen, North Rhine-Westphalia, Germany
Antonello Monti: Institute for Automation of Complex Power Systems, RWTH Aachen University, Mathieu Strasse 10, 52074 Aachen, North Rhine-Westphalia, Germany

Energies, 2025, vol. 18, issue 18, 1-32

Abstract: This paper examines artificial intelligence (AI) bias in power systems applications through systematic analysis of three critical use cases: load forecasting, predictive maintenance, and ontology matching for system interoperability. While AI solutions show great potential for addressing complex power system challenges, they face adoption barriers due to biases that compromise fairness, reliability, and operational performance. Our investigation demonstrates how different bias types—including data representation, algorithmic, and sampling biases—manifest in power systems contexts, directly affecting grid efficiency, resource allocation, and socioeconomic equity across the electrical power and energy domain. For each use case, we provide quantitative evidence of bias impact and propose targeted mitigation strategies that emphasize data diversity, ensemble methods, explainable AI techniques, and fairness-aware algorithms. By establishing a comprehensive taxonomy of bias types relevant to power systems and developing practical mitigation frameworks, this work bridges the critical gap between abstract bias concepts and real-world power system applications. The resulting framework provides a structured approach for developing equitable, robust AI systems that align with power systems’ operational requirements while accelerating the responsible adoption of AI in safety-critical infrastructure.

Keywords: AI bias; power systems; smart grid; explainable AI (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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