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A Hybrid Prediction Model for Solar Radiation Based on Long Short-Term Memory, Empirical Mode Decomposition, and Solar Profiles for Energy Harvesting Wireless Sensor Networks

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  • Yujia Ge

    (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
    School of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Yurong Nan

    (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Lijun Bai

    (School of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China)

Abstract

For power management in the energy harvesting wireless sensor networks (EH-WSNs), it is necessary to know in advance the collectable solar energy data of each node in the network. Our work aims to improve the accuracy of solar energy predictions. Therefore, several existing prediction algorithms in the literature are surveyed, and then this paper proposes a solar radiance prediction model based on a long short-term memory (LSTM) neural network in combination with the signal processing algorithm empirical mode decomposition (EMD). The EMD method is used to decompose the time sequence data into a series of relatively stable component sequences. For improving the prediction accuracy further by utilizing the current day solar radiation profile in one-hour-ahead predictions, similar solar radiation profile data were selected for training LSTM neural networks. Simulation results show that the hybrid model achieves better prediction performance than traditional prediction methods, such as the exponentially-weighted moving average (EWMA), weather conditioned moving average (WCMA), and only LSTM models.

Suggested Citation

  • Yujia Ge & Yurong Nan & Lijun Bai, 2019. "A Hybrid Prediction Model for Solar Radiation Based on Long Short-Term Memory, Empirical Mode Decomposition, and Solar Profiles for Energy Harvesting Wireless Sensor Networks," Energies, MDPI, vol. 12(24), pages 1-21, December.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4762-:d:297662
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    References listed on IDEAS

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    1. Yuqi Dong & Xuejiao Ma & Chenchen Ma & Jianzhou Wang, 2016. "Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting," Energies, MDPI, vol. 9(12), pages 1-30, December.
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    3. Moghtaderi, Azadeh & Flandrin, Patrick & Borgnat, Pierre, 2013. "Trend filtering via empirical mode decompositions," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 114-126.
    4. Utgikar, V.P. & Scott, J.P., 2006. "Energy forecasting: Predictions, reality and analysis of causes of error," Energy Policy, Elsevier, vol. 34(17), pages 3087-3092, November.
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    Cited by:

    1. Hasan Alkahtani & Theyazn H. H. Aldhyani & Saleh Nagi Alsubari, 2023. "Application of Artificial Intelligence Model Solar Radiation Prediction for Renewable Energy Systems," Sustainability, MDPI, vol. 15(8), pages 1-21, April.
    2. Yujia Ge & Yurong Nan & Xianhai Guo, 2021. "Maximizing network throughput by cooperative reinforcement learning in clustered solar-powered wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 17(4), pages 15501477211, April.
    3. Ajith, Meenu & Martínez-Ramón, Manel, 2023. "Deep learning algorithms for very short term solar irradiance forecasting: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).

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