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Parallel LSTM-Based Regional Integrated Energy System Multienergy Source-Load Information Interactive Energy Prediction

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  • Bo Wang
  • Liming Zhang
  • Hengrui Ma
  • Hongxia Wang
  • Shaohua Wan

Abstract

The multienergy interaction characteristic of regional integrated energy systems can greatly improve the efficiency of energy utilization. This paper proposes an energy prediction strategy for multienergy information interaction in regional integrated energy systems from the perspective of horizontal interaction and vertical interaction. Firstly, the multienergy information coupling correlation of the regional integrated energy system is analyzed, and the horizontal interaction and vertical interaction mode are proposed. Then, based on the long short-term memory depth neural network time series prediction, parallel long short-term memory multitask learning model is established to achieve horizontal interaction among multienergy systems and based on user-driven behavioral data to achieve vertical interaction between source and load. Finally, uncertain resources composed of wind power, photovoltaic, and various loads on both sides of source and load integrated energy prediction are achieved. The simulation results of the measured data show that the interactive parallel prediction method proposed in this article can effectively improve the prediction effect of each subtask.

Suggested Citation

  • Bo Wang & Liming Zhang & Hengrui Ma & Hongxia Wang & Shaohua Wan, 2019. "Parallel LSTM-Based Regional Integrated Energy System Multienergy Source-Load Information Interactive Energy Prediction," Complexity, Hindawi, vol. 2019, pages 1-13, November.
    • Handle: RePEc:hin:complx:7414318
    DOI: 10.1155/2019/7414318
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    1. Colak, Ilhami & Sagiroglu, Seref & Fulli, Gianluca & Yesilbudak, Mehmet & Covrig, Catalin-Felix, 2016. "A survey on the critical issues in smart grid technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 396-405.
    2. Wang, H.Z. & Wang, G.B. & Li, G.Q. & Peng, J.C. & Liu, Y.T., 2016. "Deep belief network based deterministic and probabilistic wind speed forecasting approach," Applied Energy, Elsevier, vol. 182(C), pages 80-93.
    3. Powell, Kody M. & Sriprasad, Akshay & Cole, Wesley J. & Edgar, Thomas F., 2014. "Heating, cooling, and electrical load forecasting for a large-scale district energy system," Energy, Elsevier, vol. 74(C), pages 877-885.
    4. Kang, Charles A. & Brandt, Adam R. & Durlofsky, Louis J., 2011. "Optimal operation of an integrated energy system including fossil fuel power generation, CO2 capture and wind," Energy, Elsevier, vol. 36(12), pages 6806-6820.
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    6. Gao, Chong & Lin, Junjie & Zeng, Jianfeng & Han, Fengwu, 2022. "Wind-photovoltaic co-generation prediction and energy scheduling of low-carbon complex regional integrated energy system with hydrogen industry chain based on copula-MILP," Applied Energy, Elsevier, vol. 328(C).
    7. Li, Jingrui & Wang, Jiyang & Li, Zhiwu, 2023. "A novel combined forecasting system based on advanced optimization algorithm - A study on optimal interval prediction of wind speed," Energy, Elsevier, vol. 264(C).
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    9. Mehr, A.S. & Lanzini, A. & Santarelli, M. & Rosen, Marc A., 2021. "Polygeneration systems based on high temperature fuel cell (MCFC and SOFC) technology: System design, fuel types, modeling and analysis approaches," Energy, Elsevier, vol. 228(C).
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