Data Engineering for Artificial Intelligence in Energy Systems: Toward Smarter Grids

Abstract

As the demand for sustainable and efficient energy solutions escalates, the integration of Artificial 
Intelligence (AI) into energy systems has become increasingly pivotal. This paper explores the 
role of data engineering in enhancing AI applications within energy systems, with a particular 
focus on smart grids. The study emphasizes how robust data engineering practices are essential for 
optimizing AI-driven decision-making processes in energy management. We propose a 
comprehensive framework that integrates advanced data collection, processing, and analysis 
techniques to support AI functionalities in smart grids. The framework incorporates real-time data 
acquisition, data cleaning, feature engineering, and predictive analytics to improve grid efficiency, 
reliability, and resilience. Key AI applications discussed include demand forecasting, energy 
consumption optimization, and fault detection. By leveraging case studies and experimental data, 
the paper demonstrates how effective data engineering can enhance AI performance in managing 
complex energy systems. The results highlight the potential of this approach to facilitate smarter, 
more responsive energy grids that can better meet the needs of modern societies while contributing 
to sustainability goals.