Automated Data Pipeline Optimization for Large-Scale Energy Analytics: MLOps for Energy Sector

Abstract

Electric power systems now generate data at scales that overwhelm traditional processing methods, with smart meters, renewable generators, weather sensors, and trading platforms creating continuous information streams. Machine Learning Operations emerged from the technology sector as a discipline for managing artificial intelligence in production, but power grids demand specialized adaptations that standard frameworks cannot provide. This article presents an MLOps framework built specifically for energy applications, where automated feature engineering incorporates physics-based knowledge about how electricity actually behaves. The framework tackles problems unique to utilities - measurement devices fail in harsh outdoor conditions, regulators demand explanations for every automated decision, and predictions must achieve accuracy levels that prevent blackouts and equipment damage. Real-world deployments in load forecasting, renewable generation prediction, and electricity market trading show how the framework improves forecast accuracy while meeting operational deadlines measured in milliseconds. The implementation guidance helps energy companies deploy machine learning without sacrificing the reliability standards that keep lights on across entire regions. Adaptive learning mechanisms detect when consumption patterns shift due to new technologies like electric vehicles or behavioral changes like remote work, automatically updating models to maintain accuracy. The framework proves that utilities can adopt advanced analytics while respecting the engineering principles and regulatory constraints that govern critical infrastructure.