Smart grids and machine learning: Uncovering pitfalls and improving the robustness of models

Smart grids are transforming energy systems through the integration of advanced data analytics and machine learning techniques. However, the development and deployment of these technologies present unique challenges that can significantly impact the reliability and effectiveness of the smart grid. This paper provides a study of common pitfalls in the machine learning development cycle for smart grid applications. We examine critical issues across various stages, from dataset selection to model evaluation, demonstrating how seemingly minor oversights can lead to significant errors in results and misinterpretations. Our analysis uncovers specific issues, such as the misapplication of oversampling techniques and the inappropriate use of labeled data for parameter tuning in unsupervised approaches. These practices, while often well-intentioned, can severely compromise the validity of results and lead to overly optimistic performance estimates. By illustrating examples from recent literature, this paper offers insights into the potential impact of these challenges on model performance and contributes to ongoing efforts to improve the reliability of smart grid technologies, underscoring the need for rigorous methodologies and standardized practices in this rapidly evolving field.