GENERATIVE AI IN ELECTRICITY DISTRIBUTION: A QUALITATIVE EXPLORATION

Abstract

Purpose- The purpose of this study is to explore the application and potential of generative artificial intelligence (AI) within the context of electricity distribution companies. The study aims to investigate how these advanced AI technologies, particularly Generative Adversarial Networks (GANs), can address the sector's pressing challenges, such as load forecasting, power outage prediction, and preventive maintenance. Methodology- The study employs a qualitative case study methodology, providing an in-depth analysis of real-world applications of generative AI within electricity distribution companies. The selection of cases represents a wide variety of experiences and contexts, facilitated by both primary data collected through semi-structured interviews with key personnel within the organizations and secondary data derived from an extensive review of company reports, public documentation, and industry publications. The gathered data was systematically analyzed using thematic analysis to identify and report recurring patterns and themes. Findings- The analysis reveals that generative AI has been successfully implemented in various operational aspects of electricity distribution. The first case study presents how GANs have significantly improved load forecasting accuracy in an Eastern Turkish electricity distribution company. The second case study from Southern Turkey showcases how GANs have been used for predicting power outages, thereby aiding efficient resource allocation, reducing downtime, and enhancing customer satisfaction. Lastly, the third case from Northern Turkey demonstrates how generative AI has contributed to effective preventive maintenance of distribution equipment, improving overall system reliability. Conclusion- Based on the analysis findings, it may be concluded that generative AI holds transformative potential for the electricity distribution sector. While the implementation of these technologies is associated with challenges such as data privacy, security, and the requirement of technical expertise, the benefits in terms of improved accuracy, system reliability, and resource efficiency provide a strong justification for their adoption. The paper underlines the importance of an interdisciplinary collaboration between AI researchers, electrical engineers, industry professionals, and policymakers for furthering the adoption of these technologies. As the field of generative AI continues to evolve, it is expected to have an even greater impact on the electricity distribution sector, thereby opening up exciting opportunities for future research and application. Keywords: Generative artificial intelligence (ai), electricity distribution companies, generative adversarial networks (gans), load forecasting, outage prediction, preventive maintenance JEL Codes: M40, M41