IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v239y2022ipes0360544221027134.html
   My bibliography  Save this article

Improving the accuracy of electricity and heat production forecasting in a supervision computer system of a selected gas-fired CHP plant operation

Author

Listed:
  • Szega, Marcin
  • Żymełka, Piotr
  • Janda, Tomasz

Abstract

The process of optimal planning of heat and electricity production in CHP plants is one of the most important aspects influencing economic efficiency. The effectiveness of the optimal planning results directly from the quality of generated forecasts and available calculation tools. To improve the accuracy of forecasting the production of energy carriers in the CHP plant, it has been proposed to extend the existing computer system for supervising operation with an additional calculation module. A procedure for calibrating simulation models of gas turbines based on manufacturer's correction curves used to calculate electricity production has been presented. Due to the occurring measurement redundancy, advanced data validation and reconciliation of measurements was used. In the preparation of measurement data for the development of calibration functions, the generalized likelihood ratio method was used to separate the steady-state periods of gas turbine operation. In the procedure of determining the mathematical form and coefficients of empirical functions, the cross-validation method was applied. Empirical functions for the prediction of the exhaust gases temperature at the outlet of heat recovery hot water boilers for forecasting the production of heat have been developed. Very high values of quality-of-fit measures for regression were obtained. The developed calibration models and empirical functions characterized by high quality can significantly improve the accuracy of the optimal planning for the production of energy carriers in the investigated CHP plant.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pe:s0360544221027134
    DOI: 10.1016/j.energy.2021.122464
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221027134
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.122464?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. Chenghong Gu & Da Xie & Junbo Sun & Xitian Wang & Qian Ai, 2015. "Optimal Operation of Combined Heat and Power System Based on Forecasted Energy Prices in Real-Time Markets," Energies, MDPI, vol. 8(12), pages 1-16, December.
    2. Maciej Bujalski & Paweł Madejski, 2021. "Forecasting of Heat Production in Combined Heat and Power Plants Using Generalized Additive Models," Energies, MDPI, vol. 14(8), pages 1-15, April.
    3. 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).
    4. 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.
    5. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
    6. Elmaz, Furkan & Yücel, Özgün & Mutlu, Ali Yener, 2020. "Predictive modeling of biomass gasification with machine learning-based regression methods," Energy, Elsevier, vol. 191(C).
    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.
    10. Chen, Lingen & Yang, Bo & Feng, Huijun & Ge, Yanlin & Xia, Shaojun, 2020. "Performance optimization of an open simple-cycle gas turbine combined cooling, heating and power plant driven by basic oxygen furnace gas in China's steelmaking plants," Energy, Elsevier, vol. 203(C).
    11. Fang, Tingting & Lahdelma, Risto, 2016. "Optimization of combined heat and power production with heat storage based on sliding time window method," Applied Energy, Elsevier, vol. 162(C), pages 723-732.
    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. Aliyon, Kasra & Rajaee, Fatemeh & Ritvanen, Jouni, 2023. "Use of artificial intelligence in reducing energy costs of a post-combustion carbon capture plant," Energy, Elsevier, vol. 278(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. 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).
    2. 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).
    3. Putna, Ondřej & Janošťák, František & Šomplák, Radovan & Pavlas, Martin, 2018. "Demand modelling in district heating systems within the conceptual design of a waste-to-energy plant," Energy, Elsevier, vol. 163(C), pages 1125-1139.
    4. 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).
    5. Tim Voigt & Martin Kohlhase & Oliver Nelles, 2021. "Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge," Mathematics, MDPI, vol. 9(19), pages 1-26, October.
    6. 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).
    7. Yu Liu & Shan Gao & Xin Zhao & Chao Zhang & Ningyu Zhang, 2017. "Coordinated Operation and Control of Combined Electricity and Natural Gas Systems with Thermal Storage," Energies, MDPI, vol. 10(7), pages 1-25, July.
    8. Baeten, Brecht & Confrey, Thomas & Pecceu, Sébastien & Rogiers, Frederik & Helsen, Lieve, 2016. "A validated model for mixing and buoyancy in stratified hot water storage tanks for use in building energy simulations," Applied Energy, Elsevier, vol. 172(C), pages 217-229.
    9. Beigvand, Soheil Derafshi & Abdi, Hamdi & La Scala, Massimo, 2017. "A general model for energy hub economic dispatch," Applied Energy, Elsevier, vol. 190(C), pages 1090-1111.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Luca Urbanucci & Francesco D’Ettorre & Daniele Testi, 2019. "A Comprehensive Methodology for the Integrated Optimal Sizing and Operation of Cogeneration Systems with Thermal Energy Storage," Energies, MDPI, vol. 12(5), pages 1-17, March.
    15. 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).
    16. Yu, Cao & Wang, Haizheng & Yao, Jianxi & Zhao, Jian & Sun, Qian & Zhu, Honglu, 2020. "A dynamic alarm threshold setting method for photovoltaic array and its application," Renewable Energy, Elsevier, vol. 158(C), pages 13-22.
    17. Zheng, Yingying & Jenkins, Bryan M. & Kornbluth, Kurt & Træholt, Chresten, 2018. "Optimization under uncertainty of a biomass-integrated renewable energy microgrid with energy storage," Renewable Energy, Elsevier, vol. 123(C), pages 204-217.
    18. Chen, Lingen & Xia, Shaojun, 2022. "Maximizing power output of endoreversible non-isothermal chemical engine via linear irreversible thermodynamics," Energy, Elsevier, vol. 255(C).
    19. 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.
    20. Zhang, Youjun & Xiong, Nian & Ge, Zhihua & Zhang, Yichen & Hao, Junhong & Yang, Zhiping, 2020. "A novel cascade heating system for waste heat recovery in the combined heat and power plant integrating with the steam jet pump," Applied Energy, Elsevier, vol. 278(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:239:y:2022:i:pe:s0360544221027134. 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.