In recent years, digitizing every aspect of the energy industry has become a top priority for most companies. Energy statistics related to supply, trade, stocks, transformation, and demand have already become the basis for sound energy policy decision-making. Smart energy uses modern computing power to perform tasks traditionally requiring human intelligence. Smart energy will allow physical industrial assets to be interconnected and communicate with each other through the flow of vast amounts of data in real-time.

However, smart energies and their related industries, including the oil and gas industries, face many challenges in data processing and processing in general. A large number of data banks can be created using different technologies and processes. Appropriate technical analysis of this large amount of data must be conducted to improve the performance of the Energy industries. Computational Intelligence (CI) is an interdisciplinary field covering a wide range of computing concepts including neural computing, evolutionary computing, deep learning, fuzzy computing, etc. In general, CI paradigms strive to create computing models or systems with certain useful properties.

The main aim of this Special Issue is to highlight the recent advances, developments, and challenges of CI in energy industries, including modeling, control, estimation, and optimization of electrical motors, fuel industries, renewable energy, and power systems with indications on practical and industrial applications. This Special Issue accepts submissions on the application of CI for various current issues within this field and aims to provide guidelines for future trends and research directions. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Computational Intelligence and solar thermal energy, hydropower, geothermal power, wind power, marine energy, biomass and bioenergy, energy storage and saving, energy management, smart grids, etc.
• Smart energy technologies
• Energy systems context (energy conversion, energy efficiency, energy storage, electrification, and renewable energy
• Taguchi method in energy industries
• Response surface methodology in energy industries
• Analysis of variance in energy industries
• Linear regression in energy industries
• Genetic algorithm in energy industries
• Rough sets in energy industries
• Deep Learning in energy industries