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F5: Artificial Intelligence and Smart Energy
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F5: Artificial Intelligence and Smart Energy

A section of Energies (ISSN 1996-1073).

Section Information

Increase in human growth, alongside a higher standard of living, encourages the community to engage in progressively more activities. This is evident in the massive demand for energy. Unfortunately, the current supply does not adequately meet the demands due to some challenges, including costs, techniques, technologies, resources, human skills, etc. To solve these challenges, certain approaches are utilized. However, the traditional practices, which require more items such as equipment, labour sources, procedures, etc., are tedious and time-consuming. At present, times have changed towards the era of digitalization, where all aspects of life are directed towards being fast, effective, and efficient with the assistance of the computer.  

Artificial intelligence (AI) offers a smart way to help society achieve goals in a modern manner by implementing techniques involving predictive analytics, claims analytics, emerging issues detection, survey analysis, etc. AI covers a wide range, but the fields were not formally founded until 1956, at a conference at Dartmouth College, in Hanover.

On account of drastic progress in intelligent energy systems, the AI and Smart Energy Section aims to provide a platform for showcasing the front-line research at the crossing point between AI applications, smart approaches, and energy systems. This Section also provides the latest research progress in the multidisciplinary approach of AI in the energy system, technology, development, etc. This Section considers full-length, short communications, perspective, and review articles. Focal points of the AI and Smart Energy Section include but are not limited to: 

  • Energy topics:
    • Solar thermal energy;
    • Hydropower;
    • Geothermal power;
    • Wind power;
    • Marine energy;
    • Biomass and bioenergy;
    • Hydrogen energy;
    • Nuclear energy;
    • Fossil and green fuels;
    • Energy storage and saving;
    • Energy management;
    • Smart grids;
    • Energy sustainability;
    • Energy modeling.
  • Statistic approach:
    • Taguchi method;
    • Response surface methodology;
    • Analysis of variance;
    • Linear regression;
    • Others.
  • Artificial intelligence and evolutionary computation:
    • Genetic algorithm;
    • Particle swarm optimization;
    • Nelder–Mead algorithm;
    • Multi-objective genetic algorithm;
    • Others.
  • Machine learning and data analysis:
    • Neural network;
    • Convolutional neural network;
    • Multivariate adaptive regression splines;
    • Decision tree;
    • K-means clustering;
    • Others.
 

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