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Intelligent forecast engine for short-term wind speed prediction based on stacked long short-term memory

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Abstract

This paper presents machine learning techniques for short-term wind speed prediction by exploiting their computational intelligence capabilities such as random forest regression (RFR), support vector regression (SVR), radial basis function neural networks (RBFNN), and long short-term memory (LSTM) on various wind farm datasets located in Pakistan. Initially, predictions are obtained by employing baseline regressors, RFR and RBFNN in terms of error indices. Later, a stacked LSTM forecast engine (SLFE) has been proposed to improve the efficacy, accuracy, and prediction capability of the forecast engine by using leaky rectified linear units (Leaky ReLU) as kernel function. SLFE has been implemented and tested on the datasets acquired from four different wind farms having a temporal locality of 10 min for short-term wind speed prediction. Furthermore, an ensemble of stacked LSTM with RFR, SVR, and RBFNN has also been developed for the comparison. The strength of the SLFE has been evaluated in terms of various performance measures such as mean absolute error (MAE), root mean squared error (RMSE), R2Score, and explained variance score (EVS). The efficacy of the proposed models is evaluated in terms of performance metrics, MAE, RMSE, R2Score and EVS, 0.043, 0.065, 0.813, and 0.814, respectively, demonstrating the worth of the forecast engine. Additionally, statistical one-way ANOVA is also carried out with multiple independent executions of the proposed algorithm to demonstrate the robustness, efficiency, and reliability of the model.

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Abbreviations

NN:

Neural network

ANN:

Artificial neural networks

FFBPNN:

Feed-forward backpropagation neural network

RBFNN:

Radial basis function neural network

BPNN:

Backpropagation neural network

BFGSNN:

Broyden-Fletcher-Goldfarb-Shanno neural network

GP:

Genetic programming

MLP:

Multilayer perceptron

WT:

Wavelet transform

SRNN:

Simultaneous recurrent neural network

ERNN:

Elman recurrent neural network

PSO:

Particle swarm optimization

CNN:

Convolutional neural networks

LSTM:

Long short-term memory

RFR:

Random forest regression

SVR:

Support vector regressor

RMSE:

Root mean square error

MAE:

Mean absolute error

EVS:

Explained variance score

References

  1. Foley AM, Leahy PG, McKeogh EJ (2010) Wind power forecasting & prediction methods. In: 2010 9th International conference on environment and electrical engineering, pp 61-64. IEEE

  2. Sideratos G, Hatziargyriou N (2007) An advanced statistical method for wind power forecasting. IEEE Trans power syst 22:258–265

    Article  Google Scholar 

  3. Ghorbani M et al (2016) Short-term wind speed predictions with machine learning techniques. Meteorol Atmos Phys 128(1):57–72

    Article  Google Scholar 

  4. Yilmaz I, Erik NY, Kaynar O (2010) Different types of learning algorithms of artificial neural network (ANN) models for prediction of gross calorific value (GCV) of coals. Sci Res Essays 5(16):2242–2249

    Google Scholar 

  5. Carcangiu C et al (2014) Wind gust detection and load mitigation using artificial neural networks assisted control. Wind Energy 17(7):957–970

    Article  Google Scholar 

  6. Korprasertsak N, Leephakpreeda T (2019) Robust short-term prediction of wind power generation under uncertainty via statistical interpretation of multiple forecasting models. Energy 180:387–397

    Article  Google Scholar 

  7. Ghorbani MA et al (2013) Relative importance of parameters affecting wind speed prediction using artificial neural networks. Theor Appl Climatol 114(1):107–114

    Article  Google Scholar 

  8. Haque AU, Nehrir MH, Mandal P (2014) A hybrid intelligent model for deterministic and quantile regression approach for probabilistic wind power forecasting. IEEE Trans Power Syst 29(4):1663–1672

    Article  Google Scholar 

  9. Chang W-Y (2015) Short-term load forecasting using radial basis function neural network. J Comput Commun 2015(3):40–45

    Article  Google Scholar 

  10. Sideratos G, Hatziargyriou ND (2012) Probabilistic wind power forecasting using radial basis function neural networks. IEEE Trans Power Syst 27(4):1788–1796

    Article  Google Scholar 

  11. Senjyu T et al (2006) Application of recurrent neural network to long-term-ahead generating power forecasting for wind power generator. In: 2006 IEEE PES power systems conference and exposition. IEEE.

  12. Espinoza M, Suykens JAK, Moor BD (2006) Fixed-size least squares support vector machines: a large scale application in electrical load forecasting. Comput Manag Sci 3(2):113–129

    Article  MathSciNet  Google Scholar 

  13. Hu Z, Bao Y, Xiong T (2013) Electricity load forecasting using support vector regression with memetic algorithms. Sci World J 2013:10

    Google Scholar 

  14. Duan P et al (2011) Short-term load forecasting for electric power systems using the PSO-SVR and FCM clustering techniques. Energies 4(1):173–184

    Article  Google Scholar 

  15. Yuan X et al (2017) Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine. Energy 129:122–137

    Article  Google Scholar 

  16. Alonso Á, Torres A, and Dorronsoro JR (2015) Random forests and gradient boosting for wind energy prediction. In: International conference on hybrid artificial intelligence systems. Springer.

  17. Nagy GI et al (2016) GEFCom2014: Probabilistic solar and wind power forecasting using a generalized additive tree ensemble approach. Int J Forecast 32(3):1087–1093

    Article  Google Scholar 

  18. Qureshi AS et al (2017) Wind power prediction using deep neural network based meta regression and transfer learning. Appl Soft Comput 58:742–755

    Article  Google Scholar 

  19. Zameer A et al (2017) Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks. Energy Convers Manag 134:361–372

    Article  Google Scholar 

  20. Zameer A, Khan A, Javed SG (2015) Machine learning based short term wind power prediction using a hybrid learning model. Comput Electr Eng 45:122–133

    Article  Google Scholar 

  21. Rani RHJ, Victoire AAT (2018) Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer. PLOS ONE 13(5):e0196871

    Article  Google Scholar 

  22. Deo RC et al (2018) Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data. Renew Energy 116:309–323

    Article  Google Scholar 

  23. Wang H et al (2016) Deep belief network based deterministic and probabilistic wind speed forecasting approach. Appl Energy 182:80–93

    Article  Google Scholar 

  24. Zhang Y et al (2018) Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries. IEEE Trans Veh Technol 67(7):5695–5705

    Article  Google Scholar 

  25. Xu L et al (2018) Long-short-term memory network based hybrid model for short-term electrical load forecasting. Information 9(7):165

    Article  Google Scholar 

  26. India Massana MÀ, Rodríguez Fonollosa JA, and Hernando Pericás FJ (2017) LSTM neural network-based speaker segmentation using acoustic and language modelling. In: INTERSPEECH 2017: 20–24 August 2017—Stockholm. 2017. International Speech Communication Association (ISCA)

  27. Meshram SG et al (2020) Long-term temperature trend analysis associated with agriculture crops. Theoret Appl Climatol 140(3):1139–1159

    Article  Google Scholar 

  28. Yang T et al (2018) A novel method of wind speed prediction by peephole LSTM. In: 2018 International conference on power system technology (POWERCON)

  29. Barbounis T, Theocharis JB (2007) Locally recurrent neural networks for wind speed prediction using spatial correlation. Inf Sci 177(24):5775–5797

    Article  Google Scholar 

  30. Barbounis TG et al (2006) Long-term wind speed and power forecasting using local recurrent neural network models. IEEE Trans Energy Convers 21(1):273–284

    Article  Google Scholar 

  31. Yu R et al (2019) LSTM-EFG for wind power forecasting based on sequential correlation features. Futur Gener Comput Syst 93:33–42

    Article  Google Scholar 

  32. WWEA Annual Report 2017 (2018) WWEA (World Wind Energy Association). https://wwindea.org/blog/2018/02/12/2017-statistics/

  33. Wind Mapping Project Phase-I: PMD wind mapping of coastal areas of Pakistan. http://www.pmd.gov.pk/wind/Wind_Project_files/Page558.html

  34. Ho TK (1995) Random decision forests. In: Proceedings of the third international conference on document analysis and recognition (Volume 1). IEEE Computer Society. pp. 278.

  35. Torres-Barrán A, Alonso Á, Dorronsoro JR (2019) Regression tree ensembles for wind energy and solar radiation prediction. Neurocomputing 326:151–160

    Article  Google Scholar 

  36. Breiman L (2017) Classification and regression trees. Routledge

    Book  Google Scholar 

  37. Zhao X, Wang SX, and Li T (2011) Review of evaluation criteria and main methods of wind power forecasting. In: Proceedings of international conference on smart grid and clean energy technologies (Icsgce 2011), p. 12

  38. Musavi MT et al (1992) On the training of radial basis function classifiers. Neural Netw 5(4):595–603

    Article  Google Scholar 

  39. Zeng J (2011) and W. Short-term solar power prediction using an RBF neural network, Qiao, pp 1–8

    Google Scholar 

  40. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural comput 9(8):1735–1780

    Article  Google Scholar 

  41. Barbounis TG, Theocharis JB (2006) Locally recurrent neural networks for long-term wind speed and power prediction. Neurocomputing 69(4–6):466–496

    Article  Google Scholar 

  42. Pascanu R, Bengio Y (1986) Learning to deal with long-term dependencies. Neural Comput 9:1735–1780

    Google Scholar 

  43. Zhang L et al (2018) Two-Stage short-term wind speed prediction based on LSTM-LSSVM-CFA. In: 2018 2nd IEEE conference on energy internet and energy system integration (EI2)

  44. Graves A, Jaitly N, Mohamed A-R (2013) Hybrid speech recognition with deep bidirectional LSTM. In: 2013 IEEE workshop on automatic speech recognition and understanding. IEEE

  45. Gao M et al (2019) Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM. Energy 187:115838

    Article  Google Scholar 

  46. Kim T-Y, Cho S-B (2019) Predicting residential energy consumption using CNN-LSTM neural networks. Energy 182:72–81

    Article  Google Scholar 

  47. Xu B, Wang N, Chen T, Li M (2015) Empirical evaluation of rectified activations in convolutional network. arXiv preprint arXiv:1505.00853

  48. Alternative Energy Development Board. http://www.aedb.org/index.php

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Authors and Affiliations

  1. Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad, 45650, Pakistan

    Farah Shahid, Aneela Zameer & Muhammad Javaid Iqbal

  2. Centre for Mathematical Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad, 45650, Pakistan

    Aneela Zameer

Authors
  1. Farah Shahid
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  2. Aneela Zameer
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  3. Muhammad Javaid Iqbal
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Correspondence to Aneela Zameer.

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Shahid, F., Zameer, A. & Iqbal, M.J. Intelligent forecast engine for short-term wind speed prediction based on stacked long short-term memory. Neural Comput & Applic 33, 13767–13783 (2021). https://doi.org/10.1007/s00521-021-06016-4

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