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REVIEW 3 major objections 5 minor 54 references

Wind Speed Forecasting Based on Data Decomposition and Deep Learning Models: A Case Study of a Wind Farm in Saudi Arabia

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read WPD-SAM-BiLSTM, a two-stage decomposition plus bidirectional LSTM, outperforms 27 benchmarks on Al-Jouf wind speed forecasting.

desk verdict The claimed accuracy is likely inflated by test-set hyperparameter selection and a non-causal decomposition protocol; the paper's central superiority claim is not established, though the method and case study are worth a careful re-analysis. read the letter →

arxiv 2412.13356 v1 pith:3AUVPFK7 submitted 2024-12-17 cs.LG

Yasmeen Aldossary , Nabil Hewahi , Abdulla Alasaadi This is my paper · ORCID
classification cs.LG
keywords windspeedforecastingdatadecompositionwaveletpacketseasonaladjustmentmethodbidirectionalLSTMstationarytimeseriesSaudiArabiashort-term
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes a wind speed forecasting pipeline that first decomposes hourly wind speed into frequency sub-bands with wavelet packet decomposition, then removes seasonal patterns from each sub-band, and finally forecasts each cleaned sub-series with a bidirectional LSTM before recombining the results. The central claim is that this WPD-SAM-BiLSTM model beats all 27 benchmark models on five years of hourly data from the Dumat Al-Jandal wind farm in Al-Jouf, Saudi Arabia, at one-, three-, and five-hour horizons. The reported best accuracy is a mean absolute error of 0.116901, a root mean square error of 0.152297, and an R-squared of 0.995279 for one-hour-ahead forecasts. The paper also claims that decomposition methods improve forecasting even when the wind series is stationary, a setting the authors say has received little attention. Accurate short-term wind speed forecasts matter because they help grid operators dispatch power, maintain stability, and integrate wind energy more safely.

What carries the argument

The load-bearing mechanism is the WPD-SAM-BiLSTM cascade. Wavelet packet decomposition (WPD) splits the hourly wind speed series into several frequency sub-bands, reconstructing each sub-series separately. Seasonal adjustment (SAM) then removes the seasonal component from each reconstructed sub-series and stores the seasonal indices. A single-hidden-layer bidirectional LSTM, a recurrent network that reads sequences in both time directions, forecasts each deseasonalized sub-series; the seasonal indices are added back, and all forecast sub-series are summed to produce the final wind speed forecast. The paper uses the Augmented Dickey-Fuller test to classify the data as stationary and a walk-forward training scheme so the model can adapt as new observations arrive.

What would settle it

Rerun the one-hour-ahead experiment with a strictly causal implementation: at each forecast origin, recompute the wavelet packet transform and the seasonal indices using only observations up to that origin, then measure MAE and R2 on the same test period. If the causal version's one-hour MAE is substantially larger than 0.116901, the paper's headline accuracy depends on look-ahead leakage rather than on the WPD-SAM-BiLSTM pipeline itself.

Watch

Extended reading notes

Core claim

The paper's discovery claim is that a two-stage decomposition, wavelet packet decomposition followed by seasonal adjustment, makes a bidirectional LSTM a high-accuracy short-term wind speed forecaster for the Al-Jouf site. The authors report that WPD-SAM-BiLSTM yields MAE 0.116901, RMSE 0.152297, and R2 0.995279 at one hour ahead; MAE 0.155379, RMSE 0.223525, and R2 0.989834 at three hours; and MAE 0.257366, RMSE 0.365386, and R2 0.972849 at five hours. These are the best results among the 27 benchmark models compared in the study, and the authors further claim that applying data decomposition to stationary wind speed data substantially improves deep-learning forecasts.

Load-bearing premise

The evaluation assumes that the frequency-band decomposition of the test period is computed at each forecast origin using only data available up to that moment, so the reported test errors are genuine out-of-sample errors rather than results of future values leaking into the decomposition.

Editorial extensions

If this is right

  • Operators at the Dumat Al-Jandal wind farm can use the one-, three-, and five-hour forecasts to plan grid dispatch with mean absolute errors around 0.12 to 0.26.
  • Decomposition methods previously applied mainly to non-stationary wind series also improve accuracy on stationary wind data, broadening the settings where such methods are useful.
  • Combining wavelet packet decomposition with seasonal adjustment gives a larger accuracy gain than either decomposition alone, supporting two-stage decomposition pipelines.
  • Forecast error grows with horizon, so the model's reliability is highest at the shortest lead time.
  • Because the model combines decomposition with a deep learner, it can be adapted to other wind sites by retraining the bidirectional LSTM and recomputing the site's seasonal indices.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural extension is to test whether the same WPD plus seasonal adjustment pipeline improves other deep learners on stationary wind data, not just bidirectional LSTM.
  • If the decomposition is implemented causally, the pipeline could run online and update forecasts whenever a new hourly observation arrives; the paper does not specify the implementation's causality.
  • The pipeline could be transferred to other Saudi wind sites with different wind regimes, which would show whether the WPD-SAM combination generalizes or is tuned to Al-Jouf's particular seasonality.
  • Comparing WPD-SAM-BiLSTM against a strictly causal, leakage-free implementation of the same idea would isolate the contribution of the hybrid decomposition from any look-ahead advantage.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The manuscript proposes a hybrid wind speed forecasting framework, WPD-SAM-BiLSTM, which combines Wavelet Packet Decomposition, Seasonal Adjustment Method, and Bidirectional LSTM, and applies it to five years of hourly wind speed data from the Dumat Al-Jandal wind farm in Al-Jouf, Saudi Arabia. The authors report that the proposed model outperforms 27 benchmark models across 1-, 3-, and 5-hour horizons, with headline one-hour-ahead metrics MAE 0.116901, RMSE 0.152297, and R2 0.995279. The paper also investigates the effect of data decomposition on stationary wind speed data, a topic it identifies as understudied. The central claim is the empirical superiority of the proposed model on this specific dataset.

Significance. If the reported results were obtained under a valid out-of-sample protocol, the paper would offer a useful case study for wind speed forecasting in Saudi Arabia and a substantive comparison of decomposition-based hybrids on stationary data. The manuscript has some strengths: it uses a public data source, applies an ADF stationarity test, includes a broad set of 27 benchmark models, and explicitly acknowledges in Section 2.1 the data-leakage risk of decomposing the full time series before splitting. However, the evaluation protocol as described does not implement the safeguards the authors themselves cite, and the reported accuracy numbers are therefore not trustworthy as out-of-sample evidence. The methodological issues are load-bearing for the central claim of superiority, so the contribution is not currently established.

major comments (3)
  1. [Section 3.2, steps (2)-(3)] The decomposition protocol appears to be non-causal and leaks future information into the test forecasts. Step (2) states that WPD is applied to 'the training and test signals' as blocks, and step (3) states that seasonal indices are calculated from the reconstructed subseries. A level-3 db22 WPD reconstruction at a forecast origin t uses observations well beyond t when the test block is transformed as a whole, and seasonal indices computed over the full test block encode future values. This is exactly the 'decompose-then-split' data-leakage problem the authors warn against in Section 2.1 with reference to Quilty and Adamowski [39]. The paper does not specify a causal or online implementation of WPD or SAM, so the reported MAE, RMSE, and R2 values cannot be interpreted as genuine out-of-sample forecast errors.
  2. [Section 4.1 and Table 2] The headline hyperparameters are selected on the test data, making the reported performance partly circular. Section 4.1 states that 'the proposed model was tested over different forecasting windows, WPD levels, and seasonal periods' and that the configuration 'produced the best forecasting accuracy for the proposed model; therefore, this study selected it as the proposed model's optimal configuration.' No separate validation set or nested cross-validation is described, and Table 2 lists Bayesian optimization for several BiLSTM hyperparameters without indicating which data were used for selection. Consequently, the numbers in Table 5 and the abstract are the result of test-set tuning, not an independent evaluation. This undermines the comparison with the 27 benchmark models, since the proposed model was given the advantage of test-set-based configuration search.
  3. [Section 4.1 and Section 4.2] The claim of superiority over 27 benchmarks is not supported by any uncertainty quantification. The paper reports single-run point estimates for MAE, RMSE, and R2 for each model, but no error bars, no repeated runs with different random seeds, and no statistical significance tests. Given that deep learning training is stochastic and that several benchmark differences in Table 5 are small (e.g., SWT-LSTM vs. SWT-BiLSTM at one hour), the reported ranking may reflect noise rather than systematic improvement. The authors should provide confidence intervals or significance tests, especially because the headline improvements are used to justify the practical recommendation in Section 4.2.2.
minor comments (5)
  1. [Section 4.1.1] The text says 'Compared with the best resulting models from Table 3,' but the referenced table containing the deep learning and WPD/SAM results is Table 4; Table 3 is the ADF test results.
  2. [Table 2] The row labeled 'Trial/Test epochs' appears to contain a typo; it likely should read 'Trial' or 'Training epochs.'
  3. [Table 5] In the CEEMDAN-LSTM row, the 5-hour MAE and RMSE are both listed as 0.738797; given RMSE is generally larger than MAE for non-constant errors, this is likely a transcription error and should be corrected.
  4. [Section 3.2] The paper does not specify how multi-step (3-hour and 5-hour) forecasts are generated, i.e., whether a direct strategy or a recursive strategy is used; this information is needed for reproducibility.
  5. [General] No code or reproducible implementation details are provided, and there is no data availability statement beyond citing the KAPSARC portal; sharing code would strengthen the reproducibility of the experiments.

Circularity Check

2 steps flagged · score 6.0 of 10

Central accuracy claim reduces, in part, to test-set selection and to non-causal decomposition of the test signal; the reported best-model errors are not independent out-of-sample predictions.

  1. fitted input called prediction [Section 4.1, Table 2 and text following it]
    "The proposed model was tested over different forecasting windows, WPD levels, and seasonal periods. A window size of 3, 9, and 15 for one, three, and five hours of forecasting, respectively, a three WPD level, and a seasonal period of 2192h produced the best forecasting accuracy for the proposed model; therefore, this study selected it as the proposed model's optimal configuration."

    The optimal configuration (window size, WPD level, seasonal period) is chosen because it minimizes error on the test set, and the same test set is then used to report MAE 0.116901 / R2 0.995279 and to claim superiority over 27 benchmarks. The reported test error is therefore the result of a selection procedure on that same test set, not an independent evaluation of a pre-specified model. Table 2 confirms the selection method is 'Trial/Test'.

  2. other [Section 3.2, steps (2) and (3)]
    "(2) The WPD is employed to decompose the training and test signals using the Daubechies wavelet function (db22) into several subsequences with different frequency sub-bands. ... (3) Seasonal component removal. Each reconstructed subseries derived from WPD is deseasonalized by the SAM method. Additionally, the seasonal indices are calculated."

    The paper says the test signal is decomposed as a block and seasonal indices are calculated from the reconstructed subseries. A db22 level-3 WPD reconstruction at time t has effective support extending into future test observations, and SAM indices computed over the full test block encode future values. Thus each 'forecast' at time t is built from information after t and then compared with the observed y_t. The reported errors are contaminated by this in-sample construction rather than being genuine out-of-sample forecast errors.

full rationale

The claimed derivation chain for 'WPD-SAM-BiLSTM is the best forecaster' depends on the evaluation protocol, not on a mathematical identity. The core model is not self-referential and no load-bearing self-citation or uniqueness theorem is used. However, the central empirical claim is weakened by two protocol choices visible in the paper: hyperparameters are selected by testing on the same test set whose errors are then reported, and the test-signal WPD/SAM construction appears to use future test information. These make the leading accuracy numbers partly fitted rather than predicted. Because the central claim of superiority rests on those numbers, the circularity score is 6 rather than 0-2. If the authors supplied a causal/online decomposition and a validation-set split, the same framework could be evaluated independently.

Assumptions & free parameters 5 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new entities. Its central claim depends on a cascade of two established decomposition methods, an LSTM variant, and a configuration that was tuned on the same data used for evaluation.

free parameters (5)
  • Seasonal period for SAM = 2192 hours
    Selected by testing candidate periods and choosing the one with lowest test error (Section 4.1).
  • WPD decomposition level = 3
    Selected by testing levels and choosing the one with best evaluation performance (Section 4.1).
  • Wavelet function for WPD = Daubechies db22
    Chosen as part of the configuration search; not justified independently (Section 3.2).
  • Forecasting window sizes = 3, 9, 15
    Selected per horizon to minimize test error (Section 4.1).
  • BiLSTM hyperparameters = hidden units 64, batch size 512, epochs 100, learning rate 0.0001, dropout 0.0
    Chosen by trial and Bayesian optimization on the evaluation data (Table 2).
assumptions (3)
  • domain assumption The ADF test correctly classifies the series as stationary, so decomposition methods built for stationary data are applicable.
    Section 3.2 and Table 3; stationarity is a premise for the WPD-SAM-BiLSTM framing.
  • domain assumption WPD and SAM can be composed without distorting the signal in a way that inflates forecastability.
    Section 3.1 and 3.2; the effectiveness of the cascade is asserted, not derived.
  • domain assumption The train/test split and Z-score normalization do not leak test-set statistics into model training.
    Section 3.2; the paper says normalization occurs after the split but does not specify whether normalization parameters are computed on training data only.

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Cite this review

Pith. "Pith review of Wind Speed Forecasting Based on Data Decomposition and Deep Learning Models: A Case Study of a Wind Farm in Saudi Arabia." pith.science (2026). https://pith.science/paper/3AUVPFK7

@misc{pith2026241213356,
  author       = {Pith},
  title        = {Pith review of: Wind Speed Forecasting Based on Data Decomposition and Deep Learning Models: A Case Study of a Wind Farm in Saudi Arabia},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3AUVPFK7}},
  note         = {Machine review of arXiv:2412.13356}
}
read the original abstract

With industrial and technological development and the increasing demand for electric power, wind energy has gradually become the fastest-growing and most environmentally friendly new energy source. Nevertheless, wind power generation is always accompanied by uncertainty due to the wind speed's volatility. Wind speed forecasting (WSF) is essential for power grids' dispatch, stability, and controllability, and its accuracy is crucial to effectively using wind resources. Therefore, this study proposes a novel WSF framework for stationary data based on a hybrid decomposition method and the Bidirectional Long Short-term Memory (BiLSTM) to achieve high forecasting accuracy for the Dumat Al-Jandal wind farm in Al-Jouf, Saudi Arabia. The hybrid decomposition method combines the Wavelet Packet Decomposition (WPD) and the Seasonal Adjustment Method (SAM). The SAM method eliminates the seasonal component of the decomposed subseries generated by WPD to reduce forecasting complexity. The BiLSTM is applied to forecast all the deseasonalized decomposed subseries. Five years of hourly wind speed observations acquired from a location in the Al-Jouf region were used to prove the effectiveness of the proposed model. The comparative experimental results, including 27 other models, demonstrated the proposed model's superiority in single and multiple WSF with an overall average mean absolute error of 0.176549, root mean square error of 0.247069, and R-squared error of 0.985987.

Figures

Figures reproduced from arXiv: 2412.13356 by the authors.

Figure 2
Figure 2. The forecasting errors of the proposed model, WPD￾LSTM, and SAM-LSTM 4.1.2. Experiment II This experiment was conducted to illustrate further the proposed model's superiority against the benchmark models: the hybridization of the LSTM, BiLSTM, and TCN models with other data decomposition methods, i.e., EMD, EEMD, CEEMDAN, DWT, SWT, and Seasonal-Trend decomposition based on Loess (STL). The performance metrics in for… view at source ↗

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Works this paper leans on

54 extracted references · 43 canonical work pages

  1. [39]

    Quilty and J

    J. Quilty and J. Adamowski, “Addressing the incorrect usage of wavelet-based hydrological and water resources forecasting models for real -world applications with best practices and a new fo recasting framework,” Journal of Hydrology, vol. 563, pp. 336 –353, 2018, doi: 10.1016/J.JHYDROL.2018.05.003

    work page doi:10.1016/j.jhydrol.2018.05.003 2018

  2. [1]

    British Petroleum, Bp Statistical Review of World Energy, The Energy Institute, 2022

    work page 2022

  3. [2]

    Renewable energy and climate cha nge,

    A. Olabi and M. Abdelkareem, “Renewable energy and climate cha nge,” Renewable and Sustainable Energy INTERNATIONAL JOURNAL of RENEWABLE ENERGY RESEARCH Y. Aldossary et al., Vol.13, No.3, September, 2023 1294 Reviews, vol. 158, p. 112111, April 2022, doi: 10.1016/J.RSER.2022.112111

    arXiv 2023

  4. [3]

    Ex- Ante Analysis of Economic, Social and Environmental Impacts of Large -Scale Renewable and Nucl ear Energy Targets for Global Electricity Generation by 2030,

    K. Almutairi, G. Thoma, and A. Durand -Morat, “ Ex- Ante Analysis of Economic, Social and Environmental Impacts of Large -Scale Renewable and Nucl ear Energy Targets for Global Electricity Generation by 2030,” Sustainability, vol. 10, no. 8, August 2018, doi: 10.3390/su10082884

    work page doi:10.3390/su10082884 2018

  5. [4]

    Renewable and sustainable energy production in Saudi Arabia accordi ng to Saudi Vision 2030; Current status and future prospects,

    A. Amran, M. Amran, R. Alyousef, and H. Alabduljabbar, “Renewable and sustainable energy production in Saudi Arabia accordi ng to Saudi Vision 2030; Current status and future prospects,” Journal of Cleaner Production, vol. 247, p. 119602, February 2020, doi: 10.1016/J.JCLEPRO.2019.119602

    work page arXiv 2020

  6. [5]

    The Feasibility of the First Utility-Scale Wind Farm in Saudi Arabia (The 400 MW Dumat Al -Jandal Project),

    K. Almutairi and R. Alahmadi, “The Feasibility of the First Utility-Scale Wind Farm in Saudi Arabia (The 400 MW Dumat Al -Jandal Project),” Journal of Clean Energy Technologies, vol. 10, no. 1, pp. 1 –10, March 2022, doi: 10.18178/JOCET.2022.10.1.531

    work page doi:10.18178/jocet.2022.10.1.531 2022

  7. [6]

    Shu mway and D

    R. Shu mway and D. Stoffer, Time Series Analysis and Its Applications. in Springer Texts in Statistics. Cham: Springer International Publishing, 2017 , doi: 10.1007/978-3-319-52452-8

    doi:10.1007/978-3-319-52452-8 2017

  8. [7]

    The Stationary Wavelet Transform and some Statistica l Applications,

    G. Nason and B. Silverman, “The Stationary Wavelet Transform and some Statistica l Applications,” in Wavelets and Statistics, A. Antoniadis and G. Oppenheim, Eds. New York, NY: Springer New York, 1995, pp. 281 –299, doi: 10.1007/978 -1-4612-2544- 7_17

    doi:10.1007/978 1995

Show all 54 references
  1. [8]

    Decomposition-based wind power foreca sting models and their boundary issue: An in -depth review and comprehensive discussion on potential solutions,

    Y. Chen, S. Yu, S. Islam, C. Lim, and S. Muyeen, “Decomposition-based wind power foreca sting models and their boundary issue: An in -depth review and comprehensive discussion on potential solutions,” Energy Reports, vol. 8, pp. 8805 –8820, 2022, doi: 10.1016/J.EGYR.2022.07.005

    2022 doi

  2. [9]

    Time Series Models to Simulate and Forecast Wind Speed and Wind Power,

    B. Brown, R. Katz, and A. Murphy, “Time Series Models to Simulate and Forecast Wind Speed and Wind Power,” Journal of Climate and Applied Meteorology, vol. 23, no. 8, pp. 1184 –1195, 1984, doi: 10.1175/1520 - 0450(1984)023<1184:TSMTSA>2.0.CO;2

    1984 doi

  3. [10]

    A comprehensive review on deep learning approaches in wind forecasting applications,

    Z. Wu, G. Luo, Z. Yang, Y. Guo, K. Li, and Y. Xue, “A comprehensive review on deep learning approaches in wind forecasting applications,” CAAI Transactions on Intelligence Technology, vol. 7, no. 2, pp. 129 –143, 2022, doi: https://doi.org/10.1049/cit2.12076

    2022 doi

  4. [11]

    Wind Speed Forecast ing: A Review,

    M. Silva, N. Freitas, M. dos S Silva, and M. Sakamoto, “Wind Speed Forecast ing: A Review,” Int. Journal of Engineering Research and Application, vol. 8, no. 1, pp. 4–09, 2018, doi: 10.9790/9622-0801010409

    2018 doi

  5. [12]

    A new approach to very short term wind speed prediction using K-nearest neighbor classification,

    M. Yesilbudak, S. Sagiroglu, and I. Colak, “A new approach to very short term wind speed prediction using K-nearest neighbor classification,” Energy Conversion and Management, vol. 69, pp. 77 –86, 2013. doi:10.1016/j.enconman.2013.01.033

    2013 doi

  6. [13]

    A wind speed forecasting approach based on 2 -dimensional input space,

    M. Yesilbudak, S. Sagiroglu, and I. Colak, “A wind speed forecasting approach based on 2 -dimensional input space,” in 2012 International Confer ence on Renewable Energy Research and Applications (ICRERA), 2012, pp. 1–5

    2012

  7. [15]

    Application of Bayesian model averaging in modeling long -term wind speed distributions,

    G. Li and J. Shi, “Application of Bayesian model averaging in modeling long -term wind speed distributions,” Renew Energy, vol. 35, no. 6, pp. 1192 – 1202, 2010, doi: 10.1016/J.RENENE.2009.09.003

    2010 doi

  8. [16]

    Short-term wind speed forecasting based on random forest model combining ensemble empirical mode decomposition and improved harmony search algorithm,

    M. Yu, “Short-term wind speed forecasting based on random forest model combining ensemble empirical mode decomposition and improved harmony search algorithm,” International Journal of Green Energy, vol. 17, no. 5, pp. 332 –348, 2020, doi: 10.1080/15435075.2020.1731816

    2020

  9. [17]

    Statistical approach for improved wind speed forecasting for wind power production,

    N. Pearre and L. Swan, “Statistical approach for improved wind speed forecasting for wind power production,” Sustainable Energy Technologies and Assessments, vol. 27, pp. 180 –191, 2018, doi: 10.1016/J.SETA.2018.04.010

    2018 doi

  10. [18]

    Wi nd speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output,

    F. Cassola and M. Burlando, “Wi nd speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output,” Applied energy, vol. 99, pp. 154–166, 2012

    2012

  11. [19]

    A new hybrid model for wind speed forecasting combining long short -term memory neural network, decomposition methods and grey wolf optimizer,

    A. Altan, S. Karasu, and E. Zio, “A new hybrid model for wind speed forecasting combining long short -term memory neural network, decomposition methods and grey wolf optimizer,” Appl Soft Comput, vol. 100, p. 106996, 2021, doi: 10.1016/J.ASOC.2020.106996

    2021

  12. [20]

    Wind speed prediction model using singular spectrum analysis, empirical mode decomposition and convolutional support vector machine,

    X. Mi, H. Liu, and Y. Li, “Wind speed prediction model using singular spectrum analysis, empirical mode decomposition and convolutional support vector machine,” Energy Convers Manag, vol. 180, pp. 196 – 205, Jan. 2019, doi: 10.1016/J.ENCONMAN.2018.11.006

    2019 doi

  13. [21]

    Research and application of a combined model based on variable weight for short term wind speed forecasting,

    H. Li, J. Wang, H. Lu, and Z. Guo, “Research and application of a combined model based on variable weight for short term wind speed forecasting,” Renew Energy, vol. 116, pp. 669 –684, Feb. 2018, doi: 10.1016/J.RENENE.2017.09.089

    2018 doi

  14. [22]

    Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal conv olutional networks,

    T. Qian, D. Li, F. Jiang, and M. Chen, “Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal conv olutional networks,” Energy, vol. 238, p. 121981, 2022, doi: 10.1016/J.ENERGY.2021.121981. INTERNATIONAL JOURNAL of RENEWABLE ENERGY RESEARC...

    2022

  15. [23]

    Empirical Mode Decomposition-k Nearest Neighbor Models for Wind Speed Forecasting,

    Y. Ren and P . Suganthan, “Empirical Mode Decomposition-k Nearest Neighbor Models for Wind Speed Forecasting,” Journal of Power and Energy Engineering, vol. 2, pp. 17 6–185, 2014, doi: 10.4236/jpee.2014.24025

    2014

  16. [24]

    Efficient wind speed forecasting using discrete wavelet transform and artificial neural networks,

    F. Berrezzek, K. Khelil, and T. Bouadjila, “Efficient wind speed forecasting using discrete wavelet transform and artificial neural networks,” Revue d’Intelligence Artificielle, vol. 33, no. 6, pp. 447 –452, 2019, doi: 10.18280/RIA.330607

    2019 doi

  17. [25]

    Wind Speed Forecasting Using the Stationary Wavelet Transform and Quaternion Adaptive-Gradient Methods,

    L. Saoud, H. Al -Marzouqi, and M. Deriche, “Wind Speed Forecasting Using the Stationary Wavelet Transform and Quaternion Adaptive-Gradient Methods,” IEEE Access, vol. 9, pp. 127356 –127367, 2021, doi: 10.1109/ACCESS.2021.3111667

    2021

  18. [26]

    Decomposition -based hybrid wind speed forecasting model using deep bidirectional LSTM networks,

    K. Jaseena and B. Kovoor, “Decomposition -based hybrid wind speed forecasting model using deep bidirectional LSTM networks,” Energy Convers Manag, vol. 234, p. 113944, Apr. 2021, doi: 10.1016/J.ENCONMAN.2021.113944

    2021

  19. [27]

    Wind speed forecasting models based on data decomposition, feature selection and group method of data handling network,

    Z. Duan, H. Liu, H. Wu, Y. Li, and S. Dong, “Wind speed forecasting models based on data decomposition, feature selection and group method of data handling network,” Measurement, vol. 148, p. 106971, 2019, doi: 10.1016/J.MEASUREMENT.2019.106971

    2019

  20. [28]

    X. Mi, H. Liu, and Y. Li, “Comparison of two new intelligent wind speed forecasting approaches based on Wavelet Packet Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Artificial Neural Networks,” Energy Convers Manag, vol. 155, pp. 188 –20...

    2018 doi

  21. [29]

    Medium -term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China,

    H. Jiang, J. Wang, S. Qin, and Q. Zhou, “Medium -term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China,” Renew Energy, vol. 76, pp. 91 –101, Apr. 2015, doi: 10.1016/J.RENENE.2014.11.011

    2015 doi

  22. [30]

    A novel hybrid approach for wind speed prediction,

    L. Kong, J. Wang, W. Zhang, J. Wang, and T. Han, “A novel hybrid approach for wind speed prediction,” Information Sciences, vol. 273, pp. 304 –318, 2014, doi: https://doi.org/10.1016/j.ins.2014.02.159

    2014 doi

  23. [31]

    Wind Farm Power Prediction Considering Layout and Wake Effect: Case Study of Saudi Arabia,

    K. Barashid, A. Munshi, and A. Alhindi, “Wind Farm Power Prediction Considering Layout and Wake Effect: Case Study of Saudi Arabia,” Energies, vol. 16, no. 2, p. 938, 2023, doi: 10.3390/EN16020938

    2023 doi

  24. [32]

    Univariate Time Series Prediction of Wind speed with a case study of Yanbu, Saudi Arabia,

    Y. Parvej and S. Shaahid, “Univariate Time Series Prediction of Wind speed with a case study of Yanbu, Saudi Arabia,” Inte rnational Journal of Advanced Trends in Computer Science and Engineering, vol. 10, no. 1, pp. 257 –264, 2021, doi: 10.30534/IJATCSE/2021/361012021

    2021 doi

  25. [33]

    Support vector machines for wind speed prediction,

    M. Mohandes, T. Halawani, S. Rehman, and A. Hussain, “Support vector machines for wind speed prediction,” Renew Energy, vol. 29, no. 6, pp. 939 –947, 2004, doi: 10.1016/J.RENENE.2003.11.009

    2004 doi

  26. [34]

    Forecasting Wind Speed using Artificial Neural Networks – A Case Study of a Potential Location of Saudi Arabia,

    Y. Faniband and S. Shaahid, “Forecasting Wind Speed using Artificial Neural Networks – A Case Study of a Potential Location of Saudi Arabia,” E3S Web of Conferences, vol. 173, p. 01004, 2020, doi: 10.1051/E3SCONF/202017301004

    2020

  27. [35]

    Prediction of Wind Speed Distribution Using Artificial Neural Network: The Case of Saudi Arabia,

    T. Brahimi, F. Alhebshi, H. Alnabilsi, A. Bensenouci, and M. Rahman, “Prediction of Wind Speed Distribution Using Artificial Neural Network: The Case of Saudi Arabia,” Procedia Computer Science, vol. 163, pp. 41– 48, 2019, doi: 10.1016/j.procs.2019.12.084

    2019 doi

  28. [36]

    A neural networks approach for wind speed prediction,

    T. Halawani, M. Mohandes, and S. Rehman, “A neural networks approach for wind speed prediction,” Renew Energy, vol. 13, no. 3, pp. 345 –354, 1998, doi: 10.1016/S0960-1481(98)00001-9

    1998 doi

  29. [37]

    Short term prediction of wind speed based on long - short term memory networks,

    U. Salman, S. Rehman, B. Alawode, and L. Alhems, “Short term prediction of wind speed based on long - short term memory networks,” FME Transactions, vol. 49, no. 3, pp. 643 –652, 2 021, doi: 10.5937/FME2103643S

    doi

  30. [38]

    Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects,

    M. Lipu, Md. Miah, M. Hannan, A. Hussain, M. Sarker, A. Ayob, M. Saad, and Md. Mahmud, “Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects,” IEEE Access, vol. 9, pp. 102460 –102489, 2021, doi: 10.1109/ACCES...

    2021

  31. [40]

    Short -term wind speed prediction using empirical wavelet transform and Gaussian process regression,

    J. Hu and J. Wang, “Short -term wind speed prediction using empirical wavelet transform and Gaussian process regression,” Energy, vol. 93, pp. 1456 –1466, 2015, doi: 10.1016/J.ENERGY.2015.10.041

    2015 doi

  32. [41]

    Burrus, R

    C. Burrus, R. Gopinath, and H. Guo, Wavelets and Wavelet Transforms, OpenStax-CNX, 2015

    2015

  33. [42]

    Wavelet packet transform for feature extraction of EEG during mental tasks,

    J. Xue, H. Zhang, C. Zheng, and X. Yan, “Wavelet packet transform for feature extraction of EEG during mental tasks,” In ternational Conference on Machine Learning and Cybernetics, vol. 1, pp. 360 –363, 2003, doi: 10.1109/ICMLC.2003.1264502

    2003 arXiv

  34. [43]

    Convolution wavelet packet transform and its applications to signal processing,

    B. Ye and X. Zhao, “Convolution wavelet packet transform and its applications to signal processing,” Digit Signal Process, vol. 20, no. 5, pp. 1352 –1364, 2010, doi: 10.1016/J.DSP.2010.01.007

    2010 doi

  35. [44]

    An ensemble method of full wavelet packet transform and neural network for short term electrical load forecasting,

    M. El-Hendawi and Z. Wang, “An ensemble method of full wavelet packet transform and neural network for short term electrical load forecasting,” Electric Power Systems Research, vol. 182, p. 106265, 20 20, doi: 10.1016/J.EPSR.2020.106265. INTERNATIONAL JOURNAL of RENEWABLE ENER...

  36. [45]

    A case study on a hybrid wind speed forecasting method using BP neural network,

    Z. Guo, J. Wu, H. Lu, and J. Wang, “A case study on a hybrid wind speed forecasting method using BP neural network,” Knowledge-Based Systems, vol. 24, no. 7, pp. 1048–1056, 2011, doi: 10.1016/J.KNOSYS.2011.04.019

    2011 doi

  37. [46]

    Neural network forecasting for seasonal and trend time series,

    M. Qi and P. Zhang, “Neural network forecasting for seasonal and trend time series,” European Journal of Operational Research , vol. 160, no. 2, pp. 501 –514, 2005, doi: 10.1016/J.EJOR.2003.08.037

    2005 doi

  38. [47]

    Short term load forecasting technique based on the seasonal exponential adjustment method and the regression model,

    Y. Dong, J. Wu, J. Wang, H. Lu , and X. Lu, “Short term load forecasting technique based on the seasonal exponential adjustment method and the regression model,” Energy Convers Manag, vol. 70, pp. 1 –9, Jun. 2013, doi: 10.1016/J.ENCONMAN.2013.02.010

    2013 doi

  39. [48]

    A trend fixed on firstly and seasonal adjustment model combined with the ε -SVR for short -term forecasting of electricity demand,

    D. Sun, J. Wang, W. Zhu, and W. Zhang , “A trend fixed on firstly and seasonal adjustment model combined with the ε -SVR for short -term forecasting of electricity demand,” Energy Policy, vol. 37, no. 11, pp. 4901–4909, 2009, doi: 10.1016/J.ENPOL.2009.06.046

    2009 doi

  40. [49]

    Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction,

    Z. Cui, R. Ke, Z. Pu, and Y. Wang, “ Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction,” 2018, doi: 10.48550/arxiv.1801.02143

    2018 doi arXiv

  41. [50]

    Bidirectional LSTM -RNN-based hybrid deep learning frameworks for univariate time series classification,

    M. Khan, H. Wang, A. Riaz, A. Elfatyany, and S. Karim, “Bidirectional LSTM -RNN-based hybrid deep learning frameworks for univariate time series classification,” Journal of Supercomputing, vol. 77, no. 7, pp. 7021 –7045, July 2021, doi: 10.1007/S11227-020- 03560-Z/FIGURES/6

    2021 doi

  42. [51]

    Learning Temporal and Spat ial Correlations Jointly: A Unified Framework for Wind Speed Prediction,

    Q. Zhu, J. Chen, D. Shi, L. Zhu, X. Bai, X. Duan, and Y. Liu, “Learning Temporal and Spat ial Correlations Jointly: A Unified Framework for Wind Speed Prediction,” IEEE Transactions on Sustainable Energy , vol. 11, no. 1, pp. 509 –523, January 2020, doi: 10.1109/TSTE.2019.2897136

    2020

  43. [52]

    Evaluating the wind speed persistence for several wind stations in Peninsular Malaysia,

    N. Masseran, A. Razali, K. Ibrahim, and W. Zin, “Evaluating the wind speed persistence for several wind stations in Peninsular Malaysia,” Energy, vol. 37, no. 1, pp. 649 –656, Jan uary 2012, doi: 10.1016/J.ENERGY.2011.10.035

    2012 doi

  44. [53]

    KAPSARC Data Portal,

    KAPSARC, “KAPSARC Data Portal,” 2023. https://datasource.kapsarc.org/pages/home/ (accessed Mar. 04, 2023)

    2023

  45. [54]

    Hourly wind speed forecasting based on support vector machine and artificial neural networks,

    S. Barhmi and O. Fatni, “Hourly wind speed forecasting based on support vector machine and artificial neural networks,” IAES International Journal of Artificial Intelligence, vol. 8, no. 3, p. 286, 2019

    2019

  46. [55]

    A hybrid deep learning - based neural network for 24 -h ahead wind power forecasting,

    Y. Hong an d C. Rioflorido, “A hybrid deep learning - based neural network for 24 -h ahead wind power forecasting,” Applied Energy , vol. 250, pp. 530 –539, September 2019, doi: 10.1016/J.APENERGY.2019.05.044

    2019 doi

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Reviewed August 11, 2026 · model on record in the stance chip above.