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A Hybrid Solar Irradiance Forecasting Using Full Wavelet Packet Decomposition and Bi-Directional Long Short-Term Memory (BiLSTM)

  • Research Article-Electrical Engineering
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Abstract

For the prospectus of profitability and technological advancement, solar irradiance forecasting for grid-connected solar plants is essential nowadays. However, the intermittent and stochastic nature of solar photovoltaic (PV) output has become a threat to the power security and reliability of solar-connected grids. So, in search for a stable solution, this paper proposes a hybrid model to estimate a day ahead solar irradiance by employing the full wavelet packet decomposition (FWPD) and the Bidirectional long short-term memory (BiLSTM). The FWPD extracts various frequency features and statistical characteristics of the data through decomposition process. The isolated BiLSTM network with a dropout layer is then assigned to each decomposed frequency component (sub-series), where it acts as a core predictor and obtains the futuristic value of each subseries. Finally, the final forecasting (monthly and seasonal) is obtained using the FWPD reconstruction by using averaging ensemble technique of each predicted subseries. The efficiency of the proposed model is demonstrated by comparing statistical parameters: mean absolute error (MAE), mean absolute percentage error (MAPE), root-mean-square error (RMSE), coefficient of determination (R2) and forecast skills (FS), to different contrast models: naïve (baseline) predictor, long short-term memory (LSTM), gated recurrent unit (GRU), BiLSTM and conventional wavelet transform (WT)-based BiLSTM (WTBiLSTM). The percentage improvement of proposed model in RMSE and MAPE is also discussed in this paper. In order to discuss the sensitivity with respect to difference in forecasted and observed values, various tests are conducted such as index of agreement (IA), direction change in forecasting (DC) and Diebold-Mariano hypothesis (DMH).

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The data that support the findings of this study are available from the corresponding author upon reasonable request.

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The codes of this study are available from the corresponding author upon reasonable request.

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  1. Deenbandhu Chhotu Ram University of Science and Technology, Sonepat, India

    Pardeep Singla & Manoj Duhan

  2. Guru Jambheshwar University of Science and Technology, Hisar, India

    Sumit Saroha

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  1. Pardeep Singla
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  2. Manoj Duhan
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Appendix 1

Appendix 1

Tables 15, 16, 17, and 18.

Table 15 Percentage Improvement in RMSE (γrmse) by SFM5
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Table 16 Percentage Improvement in MAPE (γmape) by SFM5
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Table 17 DC results for all models
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Table 18 Annual % FS of proposed model
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Singla, P., Duhan, M. & Saroha, S. A Hybrid Solar Irradiance Forecasting Using Full Wavelet Packet Decomposition and Bi-Directional Long Short-Term Memory (BiLSTM). Arab J Sci Eng 47, 14185–14211 (2022). https://doi.org/10.1007/s13369-022-06655-2

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