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Electricity Generation Forecast of Shanghai Municipal Solid Waste Based on Bidirectional Long Short-Term Memory Model

Author

Listed:
  • Bingchun Liu

    (School of Management, Tianjin University of Technology, Tianjin 300384, China)

  • Ningbo Zhang

    (School of Management, Tianjin University of Technology, Tianjin 300384, China)

  • Lingli Wang

    (School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China)

  • Xinming Zhang

    (School of Management Science and Real Estate, Chongqing University, Chongqing 400044, China)

Abstract

The accurate prediction of Municipal Solid Waste (MSW) electricity generation is very important for the fine management of a city. This paper selects Shanghai as the research object, through the construction of a Bidirectional Long Short-Term Memory (BiLSTM) model, and chooses six influencing factors of MSW generation as the input indicators, to realize the effective prediction of MSW generation. Then, this study obtains the MSW electricity generation capacity in Shanghai by using the aforementioned prediction results and the calculation formula of theMSW electricity generation. The experimental results show that, firstly, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) values of the BiLSTM model are 42.31, 7.390, and 63.32. Second, it is estimated that by 2025, the maximum and minimum production of MSW in Shanghai will be 17.35 million tons and 8.82 million tons under the three scenarios. Third, it is predicted that in 2025, the maximum and minimum electricity generation of Shanghai MSW under the three scenarios will be 512.752 GWh/y and 260.668 GWh/y. Finally, this paper can be used as a scientific information source for environmental sustainability decision-making for domestic MSW electricity generation technology.

Suggested Citation

  • Bingchun Liu & Ningbo Zhang & Lingli Wang & Xinming Zhang, 2022. "Electricity Generation Forecast of Shanghai Municipal Solid Waste Based on Bidirectional Long Short-Term Memory Model," IJERPH, MDPI, vol. 19(11), pages 1-16, May.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:11:p:6616-:d:826894
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    References listed on IDEAS

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    1. Ayodele, T.R. & Ogunjuyigbe, A.S.O. & Alao, M.A., 2017. "Life cycle assessment of waste-to-energy (WtE) technologies for electricity generation using municipal solid waste in Nigeria," Applied Energy, Elsevier, vol. 201(C), pages 200-218.
    2. Gómez, Antonio & Zubizarreta, Javier & Rodrigues, Marcos & Dopazo, César & Fueyo, Norberto, 2010. "Potential and cost of electricity generation from human and animal waste in Spain," Renewable Energy, Elsevier, vol. 35(2), pages 498-505.
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    Cited by:

    1. Qingchuan Zhang & Zihan Li & Wei Dong & Siwei Wei & Yingjie Liu & Min Zuo, 2023. "A Model for Predicting and Grading the Quality of Grain Storage Processes Affected by Microorganisms under Different Environments," IJERPH, MDPI, vol. 20(5), pages 1-17, February.

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