Artificial intelligence in energy industry - latest trends, technological innovations and scientific discoveries

• Autorzy: Tokarczyk Artur , Badyda Krzysztof , Woszczak Krzysztof

Warianty tytułu

PL

Sztuczna inteligencja w energetyce - najnowsze trendy, innowacje technologiczne i odkrycia naukowe

• Języki publikacji: EN

Abstrakty

EN

The aim of this paper was to perform a review of the recent research papers in the field of applications of artificial intelligence in energy sector. First, some short background on the concept of the fourth industrial revolution was given. Then, the latest innovations and trends in the field have been presented. The analysed research papers have been grouped into the following areas: general industry and power generation, anomaly detection, residential buildings and smart cities, energy market and power system. Each research paper has been discussed and its results described. Finally, a short summary of the current state of technology has been given. It should be point out that currently artificial neural networks and especially Deep Learning algorithms are the focus of most research and provide the most promising results in the area of predictive modelling and patter recognition.

PL

Celem artykułu było dokonanie przeglądu najnowszych prac badawczych z zakresu zastosowań sztucznej inteligencji w energetyce. Najpierw przedstawiono krótkie tło koncepcji czwartej rewolucji przemysłowej. Następnie zaprezentowano najnowsze innowacje i trendy w tej dziedzinie. Analizowane prace badawcze zostały pogrupowane w następujące obszary: przemysł ogólny i energetyka, detekcja anomalii, budynki mieszkalne i inteligentne miasta, rynek energii i system elektroenergetyczny. Każdy artykuł badawczy został omówiony i zostały opisane jego wyniki. Na koniec przedstawiono krótkie podsumowanie obecnego stanu technologii. Należy podkreślić, że sztuczne sieci neuronowe, a zwłaszcza algorytmy Deep Learning, są głównym przedmiotem większości badań i dostarczają najbardziej obiecujących wyników w obszarze modelowania predykcyjnego i rozpoznawania wzorców.

Słowa kluczowe

EN

artificial intelligence; machine learning; energy sector; emerging technology; Industry 4.0

PL

sztuczna inteligencja; uczenie maszynowe; energetyka; nowe technologie; Przemysł 4.0

• Wydawca: KAPRINT
• Czasopismo: Rynek Energii
• Rocznik: 2022
• Tom: Nr 4
• Strony: 72–83
• Opis fizyczny: Bibliogr. 26 poz.,fig., tab.

Twórcy

autor

Tokarczyk Artur

• Engineering Design Center, Warszawa

autor

Badyda Krzysztof

• Instytut Techniki Cieplnej Politechniki Warszawskiej

autor

Woszczak Krzysztof

• Engineering Design Center, Warszawa

Bibliografia

• [1] Ahrarinouri M., Rastegar M., Seifi A. R.: Multiagent Reinforcement Learning for Energy Management in Residential Buildings, in IEEE Transactions on Industrial Informatics, vol. 17, no. 1, pp. 659-666, Jan. 2021, https://doi.org/10.1109/TII.2020.2977104.
• [2] Ajmi A. A., Mahmood N. S., Jamaludin K. R., Habibah H., Sarip S. et al. (2022), “Intelligent Integrated Model for Improving Performance in Power Plants,” CMC-Computers, Materials & Continua, 70(3), 5783-5801, https://doi.org/10.32604/cmc.2022.021885.
• [3] Alden R. E., Gong H., Jones E. S., Ababei C., Ionel D. M.: Artificial Intelligence Method for the Forecast and Separation of Total and HVAC Loads With Application to Energy Management of Smart and NZE Homes, in IEEE Access, vol. 9, pp. 160497-160509, 2021, https://doi.org/10.1109/ACCESS.2021.3129172.
• [4] Banach M., Talaśka T., Dalecki J., Długosz R.:. New technologies for smart cities – high-resolution air pollution maps based on intelligent sensors, Concurrency Computat Pract Exper, 2020; 32:e5179, https://doi.org/10.1002/cpe.5179.
• [5] Boza P., Evgeniou T.: Artificial intelligence to support the integration of variable renewable energy sources to the power system, Applied Energy, Volume 290, 2021, 116754, ISSN 0306-2619, https://doi.org/10.1016/j.apenergy.2021.116754.
• [6] Choubey S., Karmakar G.P.: Artificial intelligence techniques and their application in oil and gas industry, Artif Intell Rev 54, 3665–3683 (2021), https://doi.org/10.1007/s10462-020-09935-1.
• [7] Dlugosz R., Kolasa M., Pedrycz W., Szulc M.: Parallel Programmable Asynchronous Neighborhood Mechanism for Kohonen SOM Implemented in CMOS Technology, in IEEE Transactions on Neural Networks, vol. 22, no. 12, pp. 2091-2104, Dec. 2011, https://doi.org/10.1109/TNN.2011.2169809.
• [8] Długosz Z., Rajewski M., Długosz R., Talaśka T.: A Novel, Low Computational Complexity, Parallel Swarm Algorithm for Application in Low-Energy Devices, Sensors 2021, 21, 8449, https://doi.org/10.3390/s21248449.
• [9] Guixue C., Zhemin Z., Qilin L., Yun L., Wenxing J.: Energy Theft Detection in an Edge Data Center Using Deep Learning, Mathematical Problems in Engineering, vol. 2021, Article ID 9938475, 12 pages, 2021, https://doi.org/10.1155/2021/9938475.
• [10] Gyumin L., Seung Jun L., Changyong L.: A convolutional neural network model for abnormality diagnosis in a nuclear power plant, Applied Soft Computing, Volume 99, 2021, 106874, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2020.106874.
• [11] Jalaee M.S., Shakibaei A., GhasemiNejad A., Jalaee S.A., Derakhshani R.,: A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran, Sustainability 2021, 13, 7612, https://doi.org/10.3390/su13147612.
• [12] Kolasa M., Długosz R., Talaśka T., Pedrycz W.: Efficient methods of initializing neuron weights in self organizing networks implemented in hardware, Applied Mathematics and Computation, Volume 319, 2018, Pages 31-47, ISSN 0096-3003, https://doi.org/10.1016/j.amc.2017.01.043.
• [13] Miraftabzadeh S.M., Longo M., Foiadelli F., Pasetti M., Igual R.: Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey, Energies 2021, 14, 4776. https://doi.org/10.3390/en14164776.
• [14] Mohapatra S.K., Mishra S., Tripathy H.K., Bhoi A.K., Barsocchi P.: A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches, Energies 2021, 14, 3900, https://doi.org/10.3390/en14133900.
• [15] Ortiz, J. H. (Ed.), (2020), Industry 4.0., Intech Open, https://doi.org/10.5772/intechopen.86000.
• [16] Sheik Mohideen Shah S., Meganathan S.: Machine learning approach for power consumption model based on monsoon data for smart cities applications, Computational Intelligence. 2021; 37: 1309-1321, https://doi.org/10.1111/coin.12368.
• [17] Subha S., Nagalakshmi S.: Design of ANFIS controller for intelligent energy management in smart grid applications, J Ambient Intell Human Comput 12, 6117–6127 (2021), https://doi.org/10.1007/s12652-020-02180-y.
• [18] Sundaram K. M., Hussain A., Sanjeevikumar P., Holm-Nielsen J. B., Kaliappan V. K., Santhoshi B. K.: Deep Learning for Fault Diagnostics in Bearings, Insulators, PV Panels, Power Lines, and Electric Vehicle Applications-The State-of-the-Art Approaches, in IEEE Access, vol. 9, pp. 41246-41260, 2021, https://doi.org/10.1109/ACCESS.2021.3064360.
• [19] Talaska T., Kolasa M., Dlugosz R.,: Parallel, Asynchronous Winner Selection Circuit for Hardware Implemented Self-Organizing Maps, 2018 25th International Conference "Mixed Design of Integrated Circuits and System" (MIXDES), 2018, pp. 184-187, https://doi.org/10.23919/MIXDES.2018.8436891.
• [20] Tanveer A., Hongyu Z., Dongdong Z., Rasikh T., Bassam A., Fasee U., AlGhamdi A., Alshamrani S., “Energetics Systems and artificial intelligence: Applications of industry 4.0,” Energy Reports, Volume 8, 2022, Pages 334-361, ISSN 2352-4847, https://doi.org/10.1016/j.egyr.2021.11.256.
• [21] Tien-Wen S., Pei-Wei T., Tarek G., Chao-Yang L.: Artificial Intelligence of Things (AIoT) Technologies and Applications, Wireless Communications and Mobile Computing, vol. 2021, Article ID 9781271, 2 pages, 2021, https://doi.org/10.1155/2021/9781271.
• [22] Yan, B., Hao, F., Meng, X.: When artificial intelligence meets building energy efficiency, a review focusing on zero energy building, Artif Intell Rev 54, 2193–2220 (2021), https://doi.org/10.1007/s10462-020-09902-w.
• [23] Yanying M., Qiang L.: Real-Time Application Optimization Control Algorithm for Energy Management Strategy of the Hybrid Power System Based on Artificial Intelligence,” Mobile Information Systems, vol. 2021, Article ID 7666834, 13 pages, 2021, https://doi.org/10.1155/2021/7666834.
• [24] Yassine H., Khalida G., Abdullah A., Faycal B., Abbes A.: Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives, Applied Energy, Volume 287, 2021, 116601, ISSN 0306-2619, https://doi.org/10.1016/j.apenergy.2021.116601.
• [25] Yousaf A., Asif R.M., Shakir M., Rehman A.U., Alassery F., Hamam H., Cheikhrouhou O.,: A Novel Machine Learning-Based Price Forecasting for Energy Management Systems, Sustainability 2021, 13, 12693, https://doi.org/10.3390/su132212693.
• [26] Zhang Y., Shi X., Zhang H., Cao Y., Terzija V.: Review on deep learning applications in frequency analysis and control of modern power system, International Journal of Electrical Power & Energy Systems, Volume 136, 2022, 107744, ISSN 0142-0615, https://doi.org/10.1016/j.ijepes.2021.107744.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).