Tracing the Evolution of Machine Learning in Energy Efficiency

The field of intelligent energy systems has witnessed a remarkable transformation owing to innovations in machine learning. Over the past few decades, the application of machine learning to energy efficiency technologies has evolved from rudimentary rule-based algorithms to highly sophisticated deep learning frameworks. This progression reflects broader trends in computational capabilities, data availability, and the growing complexity of energy systems themselves. Despite substantial technological progress, there remains a lack of comprehensive historical analyses that trace how early algorithmic approaches laid the groundwork for today’s adaptive, data-driven solutions. Ongoing debates persist over the significance of early models versus recent breakthroughs, the role of interpretable machine learning in practical deployment, and the translation of research advances into operational energy management.This Research Topic aims to provide a thorough historical review and critical assessment of the milestones in machine learning as applied to energy efficiency. By investigating key transitions—from the deployment of rule-based and expert systems to the adoption of neural networks and deep learning—the collection seeks to highlight pivotal innovations that have transformed the energy sector. Specific goals include uncovering the drivers behind major shifts in methodology, understanding the interplay between technological needs and machine learning innovation, and distilling lessons from past experiences that may guide the next generation of energy efficiency solutions. Contributors are encouraged to explore how foundational work has shaped current practices and to propose how these historical insights can help resolve present challenges and inspire future advancements.To gather further insights within the historical and technological boundaries of this Research Topic, we welcome articles addressing, but not limited to, the following themes:• Chronological reviews of machine learning techniques in energy efficiency• Analysis of the transition from rule-based to adaptive and deep learning models• Case studies of early versus contemporary intelligent energy management systems• Impact of key technological breakthroughs on energy management strategies• Evolution of data sources and their influence on model development• Lessons learned from historical applications and their relevance to emerging technologies• Critical perspectives on the translation of research innovations into real-world deployment.We invite a range of article types, including Original Research, Review, Mini-Review, and Perspective articles that illuminate the shifting landscape of machine learning in energy efficiency technologies. Topic editors: Mehdi Neshat, Chiara Bordin, Runar Unnthorsson Source: https://www.frontiersin.org/research-topics/76587/tracing-the-evolution-of-machine-learning-in-energy-efficiency