DOI: https://doi.org/10.14710/ijred.2023.48672

Wind Speed Prediction Based on Statistical and Deep Learning Models

1ENSET Mohammedia, EEIS Laboratory, Hassan II University of Casablanca, Morocco

2LICPM Laboratory, ENSA Béni Mellal, Sultan Moulay Sliman University, Béni Mellal, Morocco

Received: 1 Sep 2022; Revised: 16 Dec 2022; Accepted: 10 Jan 2023; Available online: 23 Jan 2023; Published: 15 Mar 2023.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2023 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
Wind is a dominant source of renewable energy with a high sustainability potential. However, the intermittence and unstable nature of wind source affect the efficiency and reliability of wind energy conversion systems. The prediction of the available wind potential is also heavily flawed by its unstable nature. Thus, evaluating the wind energy trough wind speed prevision, is crucial for adapting energy production to load shifting and user demand rates. This work aims to forecast the wind speed using the statistical Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model and the Deep Neural Network model of Long Short-Term Memory (LSTM). In order to shed light on these methods, a comparative analysis is conducted to select the most appropriate model for wind speed prediction. The errors metrics, mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the effectiveness of each model and are used to select the best prediction model. Overall, the obtained results showed that LSTM model, compared to SARIMA, has shown leading performance with an average of absolute percentage error (MAPE) of 14.05%.
Fulltext View|Download
Keywords: Wind power; Wind speed; Time series; Forecasting models; ARIMA; Deep learning; LSTM

Article Metrics:

Article Info
Section: Original Research Article
Language : EN
Recent articles
Theoretical and Experimental Study on the Performance of Photovoltaic using Porous Media Cooling under Indoor Condition Biodiesel Production from a Naturally Grown Green Algae Spirogyra Using Heterogeneous Catalyst: An Approach to RSM Optimization Technique Design of Optical Collimator System for Vehicle Speed Gun using Non-Imaging Optics More recent articles
Most cited articles
Kinetic and Enhancement of Biogas Production For The Purpose of Renewable Fuel Generation by Co-digestion of Cow Manure and Corn Straw in A Pilot Scale CSTR System Generating Organic Liquid Products from Catalytic Cracking of Used Cooking Oil over Mechanically Mixed Catalysts Scale up sediment microbial fuel cell for powering Led lighting Power Quality Improvement Wind Energy System Using Cascaded Multilevel Inverter Wind Turbine Rotor Simulation via CFD Based Actuator Disc Technique Compared to Detailed Measurement More cited articles
  1. Adekunle, S.A., (2017). Prédiction de la moyenne horaire de la vitesse du vent sur le site de Lomé par réseau de neurones. Sci. Appliquées Ing. 2, 1–12. http://publication.lecames.org/index.php/ing/article/view/1076
  2. Altan, A., Karasu, S., Zio, E., (2021). A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer. Appl. Soft Comput. 100, 106996. https://doi.org/10.1016/j.asoc.2020.106996
  3. Araya, I.A., Valle, C., Allende, H., (2020). A Multi-Scale Model based on the Long Short-Term Memory for day ahead hourly wind speed forecasting. Pattern Recognit. Lett. 136, 333–340. https://doi.org/10.1016/j.patrec.2019.10.011
  4. Asari, M., Nanahara, T., Maejima, T., Yamaguchi, K., Sato, T., (2002). A study on smoothing effect on output fluctuation of distributed wind power generation, in: IEEE/PES Transmission and Distribution Conference and Exhibition. Presented at the IEEE/PES Transmission and Distribution Conference and Exhibition, pp. 938–943 vol.2. https://doi.org/10.1109/TDC.2002.1177602
  5. Athiyarath, S., Paul, M., Krishnaswamy, S., (2020). A Comparative Study and Analysis of Time Series Forecasting Techniques. SN Comput. Sci. 1, 175. https://doi.org/10.1007/s42979-020-00180-5
  6. Babazadeh, H., Gao, W., Cheng, L., Lin, J., (2012). An hour ahead wind speed prediction by Kalman filter, in: 2012 IEEE Power Electronics and Machines in Wind Applications. Presented at the 2012 IEEE Power Electronics and Machines in Wind Applications, pp. 1–6. https://doi.org/10.1109/PEMWA.2012.6316394
  7. Bennitt, G.V., Schueler, T., (2012) An assessment of zenith total delay corrections from numerical weather prediction models 1. Geophysical Research Abstracts 14, EGU2012-11292, https://meetingorganizer.copernicus.org/EGU2012/EGU2012-11292.pdf
  8. Bessac, J., Constantinescu, E., Anitescu, M., (2018). Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs. Ann. Appl. Stat. 12. https://doi.org/10.1214/17-AOAS1099
  9. Botchkarev, A., (2019). A New Typology Design of Performance Metrics to Measure Errors in Machine Learning Regression Algorithms. Interdiscip. J. Inf. Knowl. Manag. 14, 045–076. https://doi.org/10.28945/4184
  10. Bououden, S., Chadli, M., Filali, S., El Hajjaji, A., (2012). Fuzzy model based multivariable predictive control of a variable speed wind turbine: LMI approach. Renew. Energy 37, 434–439. https://doi.org/10.1016/j.renene.2011.06.025
  11. Brereton, R.G., Lloyd, G.R., (2010). Support Vector Machines for classification and regression. Analyst 135, 230–267. https://doi.org/10.1039/B918972F
  12. Cassola, F., Burlando, M., (2012). Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output. Appl. Energy 99, 154–166. https://doi.org/10.1016/j.apenergy.2012.03.054
  13. Chang, Z., Zhang, Y., Chen, W., (2019). Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform. Energy 187, 115804. https://doi.org/10.1016/j.energy.2019.07.134
  14. Damousis, I.G., Alexiadis, M.C., Theocharis, J.B., Dokopoulos, P.S., (2004). A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation. IEEE Trans. Energy Convers. 19, 352–361. https://doi.org/10.1109/TEC.2003.821865
  15. Devis, A., Lipzig, N.P.M.V., Demuzere, M., (2018). Should future wind speed changes be taken into account in wind farm development? Environ. Res. Lett. 13, 064012. https://doi.org/10.1088/1748-9326/aabff7
  16. Duan, Jikai, Zuo, H., Bai, Y., Duan, Jizheng, Chang, M., Chen, B., (2021). Short-term wind speed forecasting using recurrent neural networks with error correction. Energy 217, 119397. https://doi.org/10.1016/j.energy.2020.119397
  17. Eldali, F.A., Hansen, T.M., Suryanarayanan, S., Chong, E.K.P., (2016). Employing ARIMA models to improve wind power forecasts: A case study in ERCOT, in: 2016 North American Power Symposium (NAPS). Presented at the 2016 North American Power Symposium (NAPS), pp. 1–6. https://doi.org/10.1109/NAPS.2016.7747861
  18. Elsaraiti, M., Merabet, A., (2021). A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed. Energies 14, 6782. https://doi.org/10.3390/en14206782
  19. Erdem, E., Shi, J., (2011). ARMA based approaches for forecasting the tuple of wind speed and direction. Appl. Energy 88, 1405–1414. https://doi.org/10.1016/j.apenergy.2010.10.031
  20. Faniband, Y.P., Shaahid, S.M., (2020). Forecasting Wind Speed using Artificial Neural Networks – A Case Study of a Potential Location of Saudi Arabia. E3S Web Conf. 173, 01004. https://doi.org/10.1051/e3sconf/202017301004
  21. Farida, M., Zeghdoudi, H., (2020). On Modelling seasonal ARIMA series: Comparison, Application and Forecast (Number of Injured in Road Accidents in Northeast Algeria). WSEAS Trans. Syst. Control 15, 235–246. https://doi.org/10.37394/23203.2020.15.25
  22. Geng, D., Zhang, H., Wu, H., (2020). Short-Term Wind Speed Prediction Based on Principal Component Analysis and LSTM. Appl. Sci. 10, 4416. https://doi.org/10.3390/app10134416
  23. Haddad, M., Nicod, J., Boubacar Mainassara, Y., Rabehasaina, L., Al Masry, Z., Péra, M., (2019). Wind and Solar Forecasting for Renewable Energy System using SARIMA-based Model, in: International Conference on Time Series and Forecasting. Gran Canaria, Spain
  24. Hajirahimi, Z., Khashei, M., (2019). Hybrid structures in time series modeling and forecasting: A review. Eng. Appl. Artif. Intell. 86, 83–106. https://doi.org/10.1016/j.engappai.2019.08.018
  25. Hide, C., Moore, T., Smith, M., (2003). Adaptive Kalman Filtering for Low-cost INS/GPS. J. Navig. 56, 143–152. https://doi.org/10.1017/S0373463302002151
  26. Hossin, M. and Sulaiman, M.N. (2015). A Review on Evaluation Metrics for Data Classification Evaluations. Int. J. Data Min. Knowl. Manag. Process 5, 01–11. https://doi.org/10.5121/ijdkp.2015.5201
  27. Jaseena, K.U., Kovoor, B.C., (2020). Deterministic weather forecasting models based on intelligent predictors: A survey. J. King Saud Univ. - Comput. Inf. Sci. S1319157820304729. https://doi.org/10.1016/j.jksuci.2020.09.009
  28. Kamble, V.B., Deshmukh, S.N., (2017). Comparision Between Accuracy and MSE,RMSE by Using Proposed Method with Imputation Technique. Orient. J. Comput. Sci. Technol. 10, 773–779. https://doi.org/10.13005/ojcst/10.04.11
  29. Kim, S., Kim, H., (2016). A new metric of absolute percentage error for intermittent demand forecasts. Int. J. Forecast. 32, 669–679. https://doi.org/10.1016/j.ijforecast.2015.12.003
  30. Kodjo, M.K., Bédja, K., Ajavon, A.S.A., Faye, R.M., Lishou, C., (2008). Neural networks for predictive control of the mechanism of orientation of a wind turbine. J. Sci. Pour Ing. 9, 75–85. https://doi.org/10.4314/jspi.v9i1.30061
  31. Li, G., Shi, J., Zhou, J., (2011). Bayesian adaptive combination of short-term wind speed forecasts from neural network models. Renew. Energy 36, 352–359. https://doi.org/10.1016/j.renene.2010.06.049
  32. Liu, X., Lin, Z., Feng, Z., (2021). Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM. Energy 227, 120492. https://doi.org/10.1016/j.energy.2021.120492
  33. Lorenc, A.C., (1986). Analysis methods for numerical weather prediction. Q. J. R. Meteorol. Soc. 112, 1177–1194. https://doi.org/10.1002/qj.49711247414
  34. Makridakis, S., Spiliotis, E., Assimakopoulos, V., (2018). Statistical and Machine Learning forecasting methods: Concerns and ways forward. PLOS ONE 13, e0194889. https://doi.org/10.1371/journal.pone.0194889
  35. Mantalos, P., Mattheou, K., Karagrigoriou, A., (2010). Forecasting ARMA models: a comparative study of information criteria focusing on MDIC. J. Stat. Comput. Simul. 80, 61–73. https://doi.org/10.1080/00949650802464137
  36. Martinez-García, F.P., Contreras-de-Villar, A., Muñoz-Perez, J.J., (2021). Review of Wind Models at a Local Scale: Advantages and Disadvantages. J. Mar. Sci. Eng. 9, 318. https://doi.org/10.3390/jmse9030318
  37. Mi, X., Liu, H., Li, Y., (2019). Wind speed prediction model using singular spectrum analysis, empirical mode decomposition and convolutional support vector machine. Energy Convers. Manag. 180, 196–205. https://doi.org/10.1016/j.enconman.2018.11.006
  38. Miranda, M.S., Dunn, R.W., (2006). One-hour-ahead wind speed prediction using a Bayesian methodology, in: 2006 IEEE Power Engineering Society General Meeting. Presented at the 2006 IEEE Power Engineering Society General Meeting, p. 6 pp.-. https://doi.org/10.1109/PES.2006.1709479
  39. Nair, K.R., Vanitha, V., Jisma, M., (2017). Forecasting of wind speed using ANN, ARIMA and Hybrid models, in: 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). Presented at the 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), IEEE, Kerala State,Kannur, India, pp. 170–175. https://doi.org/10.1109/ICICICT1.2017.8342555
  40. Navas, R.K.B., Prakash, S., Sasipraba, T., 2020. Artificial Neural Network based computing model for wind speed prediction: A case study of Coimbatore, Tamil Nadu, India. Phys. Stat. Mech. Its Appl. 542, 123383. https://doi.org/10.1016/j.physa.2019.123383
  41. Nazir, M.S., Alturise, F., Alshmrany, S., Nazir, H.M.J., Bilal, M., Abdalla, A.N., Sanjeevikumar, P., M. Ali, Z., (2020). Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend. Sustainability 12, 3778. https://doi.org/10.3390/su12093778
  42. Nguyen, L., Novák, V., (2019). Forecasting seasonal time series based on fuzzy techniques. Fuzzy Sets Syst. 361, 114–129. https://doi.org/10.1016/j.fss.2018.09.010
  43. Pinto, T., Ramos, S., Sousa, T.M., Vale, Z., (2014). Short-term wind speed forecasting using Support Vector Machines, in: 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE). Presented at the 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), pp. 40–46. https://doi.org/10.1109/CIDUE.2014.7007865
  44. Qian, Z., Pei, Y., Zareipour, H., Chen, N., (2019). A review and discussion of decomposition-based hybrid models for wind energy forecasting applications. Appl. Energy 235, 939–953. https://doi.org/10.1016/j.apenergy.2018.10.080
  45. Ranganayaki, V., Deepa, S.N., (2017). SVM Based Neuro Fuzzy Model for Short Term Wind Power Forecasting. Natl. Acad. Sci. Lett. 40, 131–134. https://doi.org/10.1007/s40009-016-0521-6
  46. Shivani, Sandhu, K.S., Ramachandran Nair, A., (2019). A Comparative Study of ARIMA and RNN for Short Term Wind Speed Forecasting, in: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). Presented at the 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, Kanpur, India, pp. 1–7. https://doi.org/10.1109/ICCCNT45670.2019.8944466
  47. Siami-Namini, S., Tavakoli, N., Siami Namin, A., (2018). A Comparison of ARIMA and LSTM in Forecasting Time Series, in: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). Presented at the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, Orlando, FL, pp. 1394–1401. https://doi.org/10.1109/ICMLA.2018.00227
  48. Soman, S.S., Zareipour, H., Malik, O., Mandal, P., (2010). A review of wind power and wind speed forecasting methods with different time horizons, in: North American Power Symposium 2010. Presented at the North American Power Symposium 2010, pp. 1–8. https://doi.org/10.1109/NAPS.2010.5619586
  49. Tascikaraoglu, A., Uzunoglu, M., (2014). A review of combined approaches for prediction of short-term wind speed and power. Renew. Sustain. Energy Rev. 34, 243–254. https://doi.org/10.1016/j.rser.2014.03.033
  50. Tayman, J., Swanson, D.A., 1999. On the validity of MAPE as a measure of population forecast accuracy. Popul. Res. Policy Rev. 18, 299–322. https://doi.org/10.1023/A:1006166418051
  51. Tian, Y., Xu, Y.-P., Yang, Z., Wang, G., Zhu, Q., 2018. Integration of a Parsimonious Hydrological Model with Recurrent Neural Networks for Improved Streamflow Forecasting. Water 10, 1655. https://doi.org/10.3390/w10111655
  52. Tokgöz, A., Ünal, G., 2018. A RNN based time series approach for forecasting turkish electricity load, in: 2018 26th Signal Processing and Communications Applications Conference (SIU). Presented at the 2018 26th Signal Processing and Communications Applications Conference (SIU), pp. 1–4. https://doi.org/10.1109/SIU.2018.8404313
  53. Torres, J.L., García, A., De Blas, M., De Francisco, A., 2005. Forecast of hourly average wind speed with ARMA models in Navarre (Spain). Sol. Energy 79, 65–77. https://doi.org/10.1016/j.solener.2004.09.013
  54. Wu, W., Shaikhouni, A., Donoghue, J.R., Black, M.J., 2004. Closed-loop neural control of cursor motion using a Kalman filter, in: The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Presented at the The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4126–4129. https://doi.org/10.1109/IEMBS.2004.1404151
  55. Yang, J., Astitha, M., Monache, L.D., Alessandrini, S., 2018. An Analog Technique to Improve Storm Wind Speed Prediction Using a Dual NWP Model Approach. Mon. Weather Rev. 146, 4057–4077. https://doi.org/10.1175/MWR-D-17-0198.1
  56. Yatiyana, E., Rajakaruna, S., Ghosh, A., 2017. Wind speed and direction forecasting for wind power generation using ARIMA model, in: 2017 Australasian Universities Power Engineering Conference (AUPEC). Presented at the 2017 Australasian Universities Power Engineering Conference (AUPEC), pp. 1–6. https://doi.org/10.1109/AUPEC.2017.8282494
  57. Zhang, C., Wei, H., Xie, L., Shen, Y., Zhang, K., 2016. Direct interval forecasting of wind speed using radial basis function neural networks in a multi-objective optimization framework. Neurocomputing 205, 53–63. https://doi.org/10.1016/j.neucom.2016.03.061
  58. Zhang, W., Zhang, L., Wang, J., Niu, X., 2020. Hybrid system based on a multi-objective optimization and kernel approximation for multi-scale wind speed forecasting. Appl. Energy 277, 115561. https://doi.org/10.1016/j.apenergy.2020.115561
  59. Zhou, B., Ma, X., Luo, Y., Yang, D., 2019. Wind Power Prediction Based on LSTM Networks and Nonparametric Kernel Density Estimation. IEEE Access 7, 165279–165292. https://doi.org/10.1109/ACCESS.2019.2952555

Last update:

  1. Ground Solar Irradiation Prediction Based on Feature Analysis of Ground-based Cloud Images Sequences by 3D CNN

    Xiao Cao, Zhaohong Liang, Caiqi Zhou, Guannan Bao. 2024 International Conference on Intelligent Computing and Robotics (ICICR), 2024. doi: 10.1109/ICICR61203.2024.00017

  2. Analyzing temperature distribution in pyrolysis systems using an atomic model

    Ahmad Indra Siswantara, Illa Rizianiza, Tanwir Ahmad Farhan, M. Hilman Gumelar Syafei, Dyas Prawara Mahdi, Candra Damis Widiawaty, Adi Syuriadi. AIMS Energy, 11 (6), 2023. doi: 10.3934/energy.2023048

  3. Türkiye Hurda Demir Çelik İthalatının Gelecek Değerlerinin Derin Öğrenme, Makine Öğrenmesi ve Topluluk Öğrenme Yöntemleri ile Öngörülmesi

    Yunus Emre Gür, Kamil Abdullah Eşidir. Alanya Akademik Bakış, 8 (3), 2024. doi: 10.29023/alanyaakademik.1497646

  4. Advanced Machine Learning Techniques for Accurate Very-Short-Term Wind Power Forecasting in Wind Energy Systems Using Historical Data Analysis

    G. Ponkumar, S. Jayaprakash, Karthick Kanagarathinam. Energies, 16 (14), 2023. doi: 10.3390/en16145459

  5. A Comparative Analysis of Time Series and Machine Learning Models for Wind Speed Prediction

    Rakesh Kumar, M. Prakash, B. Shakila. 2023 IEEE 3rd Mysore Sub Section International Conference (MysuruCon), 2023. doi: 10.1109/MysuruCon59703.2023.10396852

  6. Photovoltaic power prediction based on sky images and tokens-to-token vision transformer

    Qiangsheng Dai, Xuesong Huo, Dawei Su, Zhiwei Cui. International Journal of Renewable Energy Development, 12 (6), 2023. doi: 10.14710/ijred.2023.57902

Last update: 2024-11-04 08:13:19

No citation recorded.