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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

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  • 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
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    References listed on IDEAS

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    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.
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    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).

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