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

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  • 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
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    References listed on IDEAS

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    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.
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