IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v209y2020ics0360544220315395.html
   My bibliography  Save this article

Issues of an improving the accuracy of energy carriers production forecasting in a computer-aided system for monitoring the operation of a gas-fired cogeneration plant

Author

Listed:
  • Żymełka, Piotr
  • Szega, Marcin

Abstract

The article presents issues related to improving the accuracy of forecasting of combined electricity and heat production in a selected gas-fired CHP plant equipped with two gas turbines with heat exchangers and a heat accumulator. To improve the accuracy of forecasting the production of energy carriers in the considered CHP plant, it has been proposed to extend the existing computer system for supervising operation with an additional calculation module. The task of this module is to correct the forecasts for electricity and heat production obtained using gas turbine model based on correction curves. In the additional computational module of the computer system, it is proposed to use empirical functions developed with the use of operational measurements of the analysed gas turbines. In the process of preparing operational measurement data for the construction of static empirical functions, the GLR (Generalized Likelihood Ratio) algorithm was used to detect the process changes and identify the periods of operation of gas turbines in a close to steady-state. The obtained results of the analyses confirm the possibility of their use in the existing computer system of operation supervision of the CHP plant. The developed empirical models are characterized by the high quality of prediction, which is confirmed by the obtained high values of determination coefficients and low error values of the model accuracy measures. The values of the coefficient of determination R^2 are within the following ranges: from 0.9754 to 0.9936 (for gas turbine GT-1) and from 0.9138 to 0.9939 (for gas turbine GT-2). The developed empirical functions can be implemented in an additional calculation module of the computer system, increasing the accuracy of planning the production of energy carriers in a gas-fired power plant.

Suggested Citation

  • Żymełka, Piotr & Szega, Marcin, 2020. "Issues of an improving the accuracy of energy carriers production forecasting in a computer-aided system for monitoring the operation of a gas-fired cogeneration plant," Energy, Elsevier, vol. 209(C).
  • Handle: RePEc:eee:energy:v:209:y:2020:i:c:s0360544220315395
    DOI: 10.1016/j.energy.2020.118431
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220315395
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2020.118431?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Omar Mohamed & Ashraf Khalil, 2020. "Progress in Modeling and Control of Gas Turbine Power Generation Systems: A Survey," Energies, MDPI, vol. 13(9), pages 1-26, May.
    2. Thomson, Mary E. & Pollock, Andrew C. & Önkal, Dilek & Gönül, M. Sinan, 2019. "Combining forecasts: Performance and coherence," International Journal of Forecasting, Elsevier, vol. 35(2), pages 474-484.
    3. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
    4. Elmaz, Furkan & Yücel, Özgün & Mutlu, Ali Yener, 2020. "Predictive modeling of biomass gasification with machine learning-based regression methods," Energy, Elsevier, vol. 191(C).
    5. Zhou, Dengji & Yao, Qinbo & Wu, Hang & Ma, Shixi & Zhang, Huisheng, 2020. "Fault diagnosis of gas turbine based on partly interpretable convolutional neural networks," Energy, Elsevier, vol. 200(C).
    6. Mehrpanahi, Abdollah & Payganeh, Gholamhasan & Arbabtafti, Mohammadreza, 2017. "Dynamic modeling of an industrial gas turbine in loading and unloading conditions using a gray box method," Energy, Elsevier, vol. 120(C), pages 1012-1024.
    7. Szega, Marcin, 2020. "Methodology of advanced data validation and reconciliation application in industrial thermal processes," Energy, Elsevier, vol. 198(C).
    8. Plis, Marcin & Rusinowski, Henryk, 2017. "Predictive, adaptive model of PG 9171E gas turbine unit including control algorithms," Energy, Elsevier, vol. 126(C), pages 247-255.
    9. Szega, Marcin, 2018. "Extended applications of the advanced data validation and reconciliation method in studies of energy conversion processes," Energy, Elsevier, vol. 161(C), pages 156-171.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yang, Dongchuan & Guo, Ju-e & Li, Yanzhao & Sun, Shaolong & Wang, Shouyang, 2023. "Short-term load forecasting with an improved dynamic decomposition-reconstruction-ensemble approach," Energy, Elsevier, vol. 263(PA).
    2. Szega, Marcin & Żymełka, Piotr & Janda, Tomasz, 2022. "Improving the accuracy of electricity and heat production forecasting in a supervision computer system of a selected gas-fired CHP plant operation," Energy, Elsevier, vol. 239(PE).
    3. Yang, Dongchuan & Guo, Ju-e & Sun, Shaolong & Han, Jing & Wang, Shouyang, 2022. "An interval decomposition-ensemble approach with data-characteristic-driven reconstruction for short-term load forecasting," Applied Energy, Elsevier, vol. 306(PA).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Szega, Marcin & Żymełka, Piotr & Janda, Tomasz, 2022. "Improving the accuracy of electricity and heat production forecasting in a supervision computer system of a selected gas-fired CHP plant operation," Energy, Elsevier, vol. 239(PE).
    2. Cheng, Xianda & Zheng, Haoran & Yang, Qian & Zheng, Peiying & Dong, Wei, 2023. "Surrogate model-based real-time gas path fault diagnosis for gas turbines under transient conditions," Energy, Elsevier, vol. 278(PA).
    3. Thanh Dam Mai & Jaiyoung Ryu, 2021. "Effects of Damaged Rotor Blades on the Aerodynamic Behavior and Heat-Transfer Characteristics of High-Pressure Gas Turbines," Mathematics, MDPI, vol. 9(6), pages 1-21, March.
    4. Tatarczuk, Adam & Szega, Marcin & Zuwała, Jarosław, 2023. "Thermodynamic analysis of a post-combustion carbon dioxide capture process in a pilot plant equipped with a heat integrated stripper," Energy, Elsevier, vol. 278(PA).
    5. Chen Zhang & Tao Yang, 2023. "Anomaly Detection for Wind Turbines Using Long Short-Term Memory-Based Variational Autoencoder Wasserstein Generation Adversarial Network under Semi-Supervised Training," Energies, MDPI, vol. 16(19), pages 1-18, October.
    6. Li, Shuguang & Leng, Yuchi & Chaturvedi, Rishabh & Dutta, Ashit Kumar & Abdullaeva, Barno Sayfutdinovna & Fouad, Yasser, 2024. "Sustainable freshwater/energy supply through geothermal-centered layout tailored with humidification-dehumidification desalination unit; Optimized by regression machine learning techniques," Energy, Elsevier, vol. 303(C).
    7. Hou, Guolian & Gong, Linjuan & Huang, Congzhi & Zhang, Jianhua, 2020. "Fuzzy modeling and fast model predictive control of gas turbine system," Energy, Elsevier, vol. 200(C).
    8. Zhenni Ding & Huayou Chen & Ligang Zhou, 2023. "Using shapely values to define subgroups of forecasts for combining," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 905-923, July.
    9. Mingliang Bai & Jinfu Liu & Yujia Ma & Xinyu Zhao & Zhenhua Long & Daren Yu, 2020. "Long Short-Term Memory Network-Based Normal Pattern Group for Fault Detection of Three-Shaft Marine Gas Turbine," Energies, MDPI, vol. 14(1), pages 1-22, December.
    10. Tae-Hwy Lee & Ekaterina Seregina, 2020. "Learning from Forecast Errors: A New Approach to Forecast Combination," Working Papers 202024, University of California at Riverside, Department of Economics.
    11. Waqar Muhammad Ashraf & Ghulam Moeen Uddin & Syed Muhammad Arafat & Sher Afghan & Ahmad Hassan Kamal & Muhammad Asim & Muhammad Haider Khan & Muhammad Waqas Rafique & Uwe Naumann & Sajawal Gul Niazi &, 2020. "Optimization of a 660 MW e Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency," Energies, MDPI, vol. 13(21), pages 1-33, October.
    12. Cheng, Xianda & Zheng, Haoran & Dong, Wei & Yang, Xuesen, 2023. "Performance prediction of marine intercooled cycle gas turbine based on expanded similarity parameters," Energy, Elsevier, vol. 265(C).
    13. Wang, Pengfei & Zhang, Jiaxuan & Wan, Jiashuang & Wu, Shifa, 2022. "A fault diagnosis method for small pressurized water reactors based on long short-term memory networks," Energy, Elsevier, vol. 239(PC).
    14. Chen, Yu-Zhi & Tsoutsanis, Elias & Xiang, Heng-Chao & Li, Yi-Guang & Zhao, Jun-Jie, 2022. "A dynamic performance diagnostic method applied to hydrogen powered aero engines operating under transient conditions," Applied Energy, Elsevier, vol. 317(C).
    15. Sahar Safarian & Seyed Mohammad Ebrahimi Saryazdi & Runar Unnthorsson & Christiaan Richter, 2021. "Artificial Neural Network Modeling of Bioethanol Production Via Syngas Fermentation," Biophysical Economics and Resource Quality, Springer, vol. 6(1), pages 1-13, March.
    16. Wang, Yuqi & Du, Qiuwan & Li, Yunzhu & Zhang, Di & Xie, Yonghui, 2022. "Field reconstruction and off-design performance prediction of turbomachinery in energy systems based on deep learning techniques," Energy, Elsevier, vol. 238(PB).
    17. Kilic, Ugur & Yalin, Gorkem & Cam, Omer, 2023. "Digital twin for Electronic Centralized Aircraft Monitoring by machine learning algorithms," Energy, Elsevier, vol. 283(C).
    18. Loyola-Fuentes, José & Smith, Robin, 2019. "Data reconciliation and gross error detection in crude oil pre-heat trains undergoing shell-side and tube-side fouling deposition," Energy, Elsevier, vol. 183(C), pages 368-384.
    19. Pourali, Mostafa & Esfahani, Javad Abolfazli, 2022. "Performance analysis of a micro-scale integrated hydrogen production system by analytical approach, machine learning, and response surface methodology," Energy, Elsevier, vol. 255(C).
    20. Ascher, Simon & Watson, Ian & You, Siming, 2022. "Machine learning methods for modelling the gasification and pyrolysis of biomass and waste," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:209:y:2020:i:c:s0360544220315395. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.