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Advanced AI Applications in Energy and Environmental Engineering Systems

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  • Jaroslaw Krzywanski

    (Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland)

Abstract

Artificial intelligence (AI) constitutes a kind of modelling method widely used in various fields of science including energy and environmental engineering [...]

Suggested Citation

  • Jaroslaw Krzywanski, 2022. "Advanced AI Applications in Energy and Environmental Engineering Systems," Energies, MDPI, vol. 15(15), pages 1-3, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5621-:d:878942
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    References listed on IDEAS

    as
    1. Ahmed Abu-Siada, 2019. "Improved Consistent Interpretation Approach of Fault Type within Power Transformers Using Dissolved Gas Analysis and Gene Expression Programming," Energies, MDPI, vol. 12(4), pages 1-13, February.
    2. Naser Hossein Motlagh & Mahsa Mohammadrezaei & Julian Hunt & Behnam Zakeri, 2020. "Internet of Things (IoT) and the Energy Sector," Energies, MDPI, vol. 13(2), pages 1-27, January.
    3. Miroslav Crnogorac & Miloš Tanasijević & Dušan Danilović & Vesna Karović Maričić & Branko Leković, 2020. "Selection of Artificial Lift Methods: A Brief Review and New Model Based on Fuzzy Logic," Energies, MDPI, vol. 13(7), pages 1-15, April.
    4. Mohammed Alkahtani & Yihua Hu & Zuyu Wu & Colin Sokol Kuka & Muflih S. Alhammad & Chen Zhang, 2020. "Gene Evaluation Algorithm for Reconfiguration of Medium and Large Size Photovoltaic Arrays Exhibiting Non-Uniform Aging," Energies, MDPI, vol. 13(8), pages 1-19, April.
    5. Pradyot Ranjan Jena & Shunsuke Managi & Babita Majhi, 2021. "Forecasting the CO 2 Emissions at the Global Level: A Multilayer Artificial Neural Network Modelling," Energies, MDPI, vol. 14(19), pages 1-23, October.
    6. Dorian Skrobek & Jaroslaw Krzywanski & Marcin Sosnowski & Anna Kulakowska & Anna Zylka & Karolina Grabowska & Katarzyna Ciesielska & Wojciech Nowak, 2020. "Prediction of Sorption Processes Using the Deep Learning Methods (Long Short-Term Memory)," Energies, MDPI, vol. 13(24), pages 1-16, December.
    7. Safoura Zadhossein & Yousef Abbaspour-Gilandeh & Mohammad Kaveh & Mariusz Szymanek & Esmail Khalife & Olusegun D. Samuel & Milad Amiri & Jacek Dziwulski, 2021. "Exergy and Energy Analyses of Microwave Dryer for Cantaloupe Slice and Prediction of Thermodynamic Parameters Using ANN and ANFIS Algorithms," Energies, MDPI, vol. 14(16), pages 1-19, August.
    8. Krzysztof Górnicki & Radosław Winiczenko & Agnieszka Kaleta, 2019. "Estimation of the Biot Number Using Genetic Algorithms: Application for the Drying Process," Energies, MDPI, vol. 12(14), pages 1-22, July.
    9. Ivan Lorencin & Nikola Anđelić & Vedran Mrzljak & Zlatan Car, 2019. "Genetic Algorithm Approach to Design of Multi-Layer Perceptron for Combined Cycle Power Plant Electrical Power Output Estimation," Energies, MDPI, vol. 12(22), pages 1-26, November.
    10. Mehdi Jadidi & Stevan Kostic & Leonardo Zimmer & Seth B. Dworkin, 2020. "An Artificial Neural Network for the Low-Cost Prediction of Soot Emissions," Energies, MDPI, vol. 13(18), pages 1-27, September.
    11. Shokufe Afzali & Mohamad Mohamadi-Baghmolaei & Sohrab Zendehboudi, 2021. "Application of Gene Expression Programming (GEP) in Modeling Hydrocarbon Recovery in WAG Injection Process," Energies, MDPI, vol. 14(21), pages 1-28, November.
    12. Fazli Wahid & Muhammad Fayaz & Ayman Aljarbouh & Masood Mir & Muhammad Aamir & Imran, 2020. "Energy Consumption Optimization and User Comfort Maximization in Smart Buildings Using a Hybrid of the Firefly and Genetic Algorithms," Energies, MDPI, vol. 13(17), pages 1-26, August.
    13. Kasım Zor & Özgür Çelik & Oğuzhan Timur & Ahmet Teke, 2020. "Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks," Energies, MDPI, vol. 13(5), pages 1-24, March.
    14. David Toquica & Kodjo Agbossou & Roland Malhamé & Nilson Henao & Sousso Kelouwani & Alben Cardenas, 2020. "Adaptive Machine Learning for Automated Modeling of Residential Prosumer Agents," Energies, MDPI, vol. 13(9), pages 1-19, May.
    15. Thomas Huybrechts & Philippe Reiter & Siegfried Mercelis & Jeroen Famaey & Steven Latré & Peter Hellinckx, 2021. "Automated Testbench for Hybrid Machine Learning-Based Worst-Case Energy Consumption Analysis on Batteryless IoT Devices," Energies, MDPI, vol. 14(13), pages 1-26, June.
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