Machine Learning for Sustainable Energy Solutions

The 1st Edition of Machine Learning for Sustainable Energy Solutions, authored by Zafar Said and Prabhakar Sharma and published by Wiley-Blackwell, provides a focused reference on applying advanced computational tools to clean energy infrastructure. This reference volume details how modern engineering methodologies including machine learning, artificial intelligence, digital twins, nanotechnology, and the Internet of Things interact directly with renewable energy applications.

The work organizes its coverage around primary infrastructure requirements, providing thorough reviews of chemical, thermal, electrical, and mechanical energy storage techniques. By examining computational strategies alongside physical energy storage systems, the text addresses both mathematical concepts and physical operation principles within clean power facilities.

Each individual chapter is founded on current academic research and enhanced by experimental or simulated data sets. This empirical format gives professionals, researchers, educators, and students a structured reference, while offering institutional decision-makers the evidence required to make informed choices regarding sustainable practices and corporate policy.