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Pumps-as-Turbines’ (PaTs) performance prediction improvement using evolutionary artificial neural networks

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  • Telikani, Akbar
  • Rossi, Mosé
  • Khajehali, Naghmeh
  • Renzi, Massimiliano

Abstract

Energy production from clean sources is mandatory to reduce pollutant emissions. Among different options for hidden hydropower potential exploitation, Pump-as-Turbine (PaT) represents a viable solution in pico- and micro-hydropower applications for its flexibility and low-cost. Pumps are widely available in the global market in terms of both sizes and spare parts. To date, there are several PaTs’ performance prediction models in the literature, but very few of them use optimization algorithms and only for specific and limited prediction goals. The present work proposes evolutionary Artificial Neural Networks (ANNs) based on JADE, which is a typology of differential evolution algorithm, to forecast Best Efficiency Point (BEP) and performance curves of a PaT starting from the pump operational data. In this model, JADE is employed as optimizer of basic ANNs to upgrade parameter values of the learning rate, weights, and biases. The accuracy of the proposed model is evaluated through experimental data available from the literature and compared to a basic ANN and two versions of the differential evolution algorithm. Results are also validated with experiments on a PaT showing that the proposed method can achieve an average R2-value of 0.97, which is 5% higher than the one obtained with a basic ANN.

Suggested Citation

  • Telikani, Akbar & Rossi, Mosé & Khajehali, Naghmeh & Renzi, Massimiliano, 2023. "Pumps-as-Turbines’ (PaTs) performance prediction improvement using evolutionary artificial neural networks," Applied Energy, Elsevier, vol. 330(PA).
    • Handle: RePEc:eee:appene:v:330:y:2023:i:pa:s0306261922015732
    DOI: 10.1016/j.apenergy.2022.120316
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    1. Moazeni, Faegheh & Khazaei, Javad, 2021. "Optimal energy management of water-energy networks via optimal placement of pumps-as-turbines and demand response through water storage tanks," Applied Energy, Elsevier, vol. 283(C).
    2. Wu, Yunna & Zhang, Ting & Gao, Rui & Wu, Chenghao, 2021. "Portfolio planning of renewable energy with energy storage technologies for different applications from electricity grid," Applied Energy, Elsevier, vol. 287(C).
    3. Renzi, Massimiliano & Rudolf, Pavel & Štefan, David & Nigro, Alessandra & Rossi, Mosè, 2019. "Installation of an axial Pump-as-Turbine (PaT) in a wastewater sewer of an oil refinery: A case study," Applied Energy, Elsevier, vol. 250(C), pages 665-676.
    4. Koponen, Kati & Le Net, Elisabeth, 2021. "Towards robust renewable energy investment decisions at the territorial level," Applied Energy, Elsevier, vol. 287(C).
    5. Renzi, Massimiliano & Nigro, Alessandra & Rossi, Mosè, 2020. "A methodology to forecast the main non-dimensional performance parameters of pumps-as-turbines (PaTs) operating at Best Efficiency Point (BEP)," Renewable Energy, Elsevier, vol. 160(C), pages 16-25.
    6. Guijo-Rubio, D. & Durán-Rosal, A.M. & Gutiérrez, P.A. & Gómez-Orellana, A.M. & Casanova-Mateo, C. & Sanz-Justo, J. & Salcedo-Sanz, S. & Hervás-Martínez, C., 2020. "Evolutionary artificial neural networks for accurate solar radiation prediction," Energy, Elsevier, vol. 210(C).
    7. Solangi, K.H. & Islam, M.R. & Saidur, R. & Rahim, N.A. & Fayaz, H., 2011. "A review on global solar energy policy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 2149-2163, May.
    8. Huang, Si & Qiu, Guangqi & Su, Xianghui & Chen, Junrong & Zou, Wenlang, 2017. "Performance prediction of a centrifugal pump as turbine using rotor-volute matching principle," Renewable Energy, Elsevier, vol. 108(C), pages 64-71.
    9. Rossi, Mosè & Renzi, Massimiliano, 2018. "A general methodology for performance prediction of pumps-as-turbines using Artificial Neural Networks," Renewable Energy, Elsevier, vol. 128(PA), pages 265-274.
    10. Golizadeh Akhlaghi, Yousef & Aslansefat, Koorosh & Zhao, Xudong & Sadati, Saba & Badiei, Ali & Xiao, Xin & Shittu, Samson & Fan, Yi & Ma, Xiaoli, 2021. "Hourly performance forecast of a dew point cooler using explainable Artificial Intelligence and evolutionary optimisations by 2050," Applied Energy, Elsevier, vol. 281(C).
    11. Chacón, Miguel Crespo & Rodríguez Díaz, Juan Antonio & Morillo, Jorge García & McNabola, Aonghus, 2021. "Evaluation of the design and performance of a micro hydropower plant in a pressurised irrigation network: Real world application at farm-level in Southern Spain," Renewable Energy, Elsevier, vol. 169(C), pages 1106-1120.
    12. Ogunmodede, Oluwaseun & Anderson, Kate & Cutler, Dylan & Newman, Alexandra, 2021. "Optimizing design and dispatch of a renewable energy system," Applied Energy, Elsevier, vol. 287(C).
    13. Venturini, Mauro & Manservigi, Lucrezia & Alvisi, Stefano & Simani, Silvio, 2018. "Development of a physics-based model to predict the performance of pumps as turbines," Applied Energy, Elsevier, vol. 231(C), pages 343-354.
    14. Morabito, Alessandro & Hendrick, Patrick, 2019. "Pump as turbine applied to micro energy storage and smart water grids: A case study," Applied Energy, Elsevier, vol. 241(C), pages 567-579.
    15. Singh, Priyanka & Dwivedi, Pragya, 2018. "Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem," Applied Energy, Elsevier, vol. 217(C), pages 537-549.
    16. Stefanizzi, Michele & Capurso, Tommaso & Balacco, Gabriella & Binetti, Mario & Camporeale, Sergio Mario & Torresi, Marco, 2020. "Selection, control and techno-economic feasibility of Pumps as Turbines in Water Distribution Networks," Renewable Energy, Elsevier, vol. 162(C), pages 1292-1306.
    17. Rossi, Mosè & Nigro, Alessandra & Renzi, Massimiliano, 2019. "Experimental and numerical assessment of a methodology for performance prediction of Pumps-as-Turbines (PaTs) operating in off-design conditions," Applied Energy, Elsevier, vol. 248(C), pages 555-566.
    18. Kati Koponen & Elisabeth Le Net, 2021. "Towards robust renewable energy investment decisions at the territorial level," Post-Print cea-03216464, HAL.
    19. Bekker, A. & Van Dijk, M. & Niebuhr, C.M., 2022. "A review of low head hydropower at wastewater treatment works and development of an evaluation framework for South Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
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