LSTM vs. Transformer Models in Power Forecasting: A Comprehensive Survey

Accurate forecasting of electricity consumption is essential for optimizing energy resources, load balancing, and grid reliability. As urbanization and the integration of renewable energy accelerate, sophisticated forecasting models become indispensable. Long Short-Term Memory (LSTM) networks have long been relied upon for sequential prediction due to their effective memory architecture. More recently, Transformer models—originally developed for Natural Language Processing—have emerged as powerful alternatives, offering enhanced scalability and superior long-range dependency modeling. This survey provides a detailed comparative analysis of and Transformer-based models for electricity usage forecasting. We evaluated 20 peer-reviewed studies by examining forecasting accuracy, scalability, infrastructure compatibility, and deployment viability. Our review finds that while Transformer models excel at long-range, high-resolution forecasting, LSTMs are valuable for lightweight, real-time applications. We also highlight promising hybrid models that integrate both paradigms. Finally, we discuss the critical impact of machine learning infrastructure and propose future research directions to enhance performance and adaptability.