AI Bias in Power Systems Domain—Exemplary Cases and Approaches

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.