Critical Review of Artificial Intelligence and Machine Learning Methods for Data-Center Load Forecasting
TL;DR
The reviewed studies indicate that advanced approaches, including long short-term memory (LSTM), gated recurrent unit (GRU), transformer-based models, and digital twin (DT) frameworks, can improve forecasting performance and support more energy-efficient operation.
Abstract
Modern data centers are consuming more energy than ever before due to the rapid growth of cloud services, artificial intelligence (AI), and large-scale digital applications. As energy demand continues to rise, accurate load forecasting has become an important tool for improving energy management and operational planning. However, predicting data-center power demand remains challenging because computing workloads, cooling systems, and facility operations are closely connected and constantly changing. This review examines the use of artificial intelligence (AI) and machine learning (ML) techniques for data-center load forecasting. The surveyed literature covers traditional statistical methods, supervised learning algorithms, deep learning models, probabilistic forecasting techniques, and physics-informed hybrid approaches. Important topics such as forecasting horizons, feature selection, performance evaluation, and practical deployment challenges are also discussed. The reviewed studies indicate that advanced approaches, including long short-term memory (LSTM), gated recurrent unit (GRU), transformer-based models, and digital twin (DT) frameworks, can improve forecasting performance and support more energy-efficient operation. These methods can also assist carbon-aware and grid-interactive data-center management. Several challenges remain. These include limited data availability, poor generalization, interpretability issues, and real-time implementation constraints. In this paper, the reviewed studies are classified and analyzed to provide a clear reference for researchers working in AI-based data-center energy forecasting.