IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v16y2012i7p4563-4573.html
   My bibliography  Save this article

A new strategy for predicting short-term wind speed using soft computing models

Author

Listed:
  • Haque, Ashraf U.
  • Mandal, Paras
  • Kaye, Mary E.
  • Meng, Julian
  • Chang, Liuchen
  • Senjyu, Tomonobu

Abstract

Wind power prediction is a widely used tool for the large-scale integration of intermittent wind-powered generators into power systems. Given the cubic relationship between wind speed and wind power, accurate forecasting of wind speed is imperative for the estimation of future wind power generation output. This paper presents a performance analysis of short-term wind speed prediction techniques based on soft computing models (SCMs) formulated on a backpropagation neural network (BPNN), a radial basis function neural network (RBFNN), and an adaptive neuro-fuzzy inference system (ANFIS). The forecasting performance of the SCMs is augmented by a similar days (SD) method, which considers similar historical weather information corresponding to the forecasting day in order to determine similar wind speed days for processing. The test results demonstrate that all evaluated SCMs incur some level of performance improvement with the addition of SD pre-processing. As an example, the SD+ANFIS model can provide up to 48% improvement in forecasting accuracy when compared to the individual ANFIS model alone.

Suggested Citation

  • Haque, Ashraf U. & Mandal, Paras & Kaye, Mary E. & Meng, Julian & Chang, Liuchen & Senjyu, Tomonobu, 2012. "A new strategy for predicting short-term wind speed using soft computing models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4563-4573.
    • Handle: RePEc:eee:rensus:v:16:y:2012:i:7:p:4563-4573
    DOI: 10.1016/j.rser.2012.05.042
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032112003760
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2012.05.042?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Shi, Jing & Guo, Jinmei & Zheng, Songtao, 2012. "Evaluation of hybrid forecasting approaches for wind speed and power generation time series," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3471-3480.
    2. Ramirez-Rosado, Ignacio J. & Fernandez-Jimenez, L. Alfredo & Monteiro, Cláudio & Sousa, João & Bessa, Ricardo, 2009. "Comparison of two new short-term wind-power forecasting systems," Renewable Energy, Elsevier, vol. 34(7), pages 1848-1854.
    3. Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
    4. Blonbou, Ruddy, 2011. "Very short-term wind power forecasting with neural networks and adaptive Bayesian learning," Renewable Energy, Elsevier, vol. 36(3), pages 1118-1124.
    5. Georgilakis, Pavlos S., 2008. "Technical challenges associated with the integration of wind power into power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(3), pages 852-863, April.
    6. Costa, Alexandre & Crespo, Antonio & Navarro, Jorge & Lizcano, Gil & Madsen, Henrik & Feitosa, Everaldo, 2008. "A review on the young history of the wind power short-term prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(6), pages 1725-1744, August.
    7. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.
    8. Lei, Ma & Shiyan, Luan & Chuanwen, Jiang & Hongling, Liu & Yan, Zhang, 2009. "A review on the forecasting of wind speed and generated power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 915-920, May.
    9. Salcedo-Sanz, Sancho & Ángel M. Pérez-Bellido, & Ortiz-García, Emilio G. & Portilla-Figueras, Antonio & Prieto, Luis & Paredes, Daniel, 2009. "Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction," Renewable Energy, Elsevier, vol. 34(6), pages 1451-1457.
    10. Cadenas, Erasmo & Rivera, Wilfrido, 2010. "Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model," Renewable Energy, Elsevier, vol. 35(12), pages 2732-2738.
    11. De Giorgi, Maria Grazia & Ficarella, Antonio & Tarantino, Marco, 2011. "Error analysis of short term wind power prediction models," Applied Energy, Elsevier, vol. 88(4), pages 1298-1311, April.
    12. Hering, Amanda S. & Genton, Marc G., 2010. "Powering Up With Space-Time Wind Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 92-104.
    13. Bilgili, Mehmet & Sahin, Besir & Yasar, Abdulkadir, 2007. "Application of artificial neural networks for the wind speed prediction of target station using reference stations data," Renewable Energy, Elsevier, vol. 32(14), pages 2350-2360.
    14. Li, Gong & Shi, Jing & Zhou, Junyi, 2011. "Bayesian adaptive combination of short-term wind speed forecasts from neural network models," Renewable Energy, Elsevier, vol. 36(1), pages 352-359.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Jianzhou & Xiong, Shenghua, 2014. "A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China," Energy, Elsevier, vol. 76(C), pages 526-541.
    2. Wang, Jianzhou & Qin, Shanshan & Zhou, Qingping & Jiang, Haiyan, 2015. "Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China," Renewable Energy, Elsevier, vol. 76(C), pages 91-101.
    3. Wang, Jianzhou & Song, Yiliao & Liu, Feng & Hou, Ru, 2016. "Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 960-981.
    4. Hu, Jianming & Wang, Jianzhou & Xiao, Liqun, 2017. "A hybrid approach based on the Gaussian process with t-observation model for short-term wind speed forecasts," Renewable Energy, Elsevier, vol. 114(PB), pages 670-685.
    5. Shamshirband, Shahaboddin & Keivani, Afram & Mohammadi, Kasra & Lee, Malrey & Hamid, Siti Hafizah Abd & Petkovic, Dalibor, 2016. "Assessing the proficiency of adaptive neuro-fuzzy system to estimate wind power density: Case study of Aligoodarz, Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 429-435.
    6. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    7. Dong, Zhen & Li, Zhongguo & Liang, Zhongchao & Xu, Yiqiao & Ding, Zhengtao, 2021. "Distributed neural network enhanced power generation strategy of large-scale wind power plant for power expansion," Applied Energy, Elsevier, vol. 303(C).
    8. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    9. Hu, Jianming & Wang, Jianzhou & Ma, Kailiang, 2015. "A hybrid technique for short-term wind speed prediction," Energy, Elsevier, vol. 81(C), pages 563-574.
    10. Liu, Hui & Tian, Hong-qi & Pan, Di-fu & Li, Yan-fei, 2013. "Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks," Applied Energy, Elsevier, vol. 107(C), pages 191-208.
    11. Hannah Jessie Rani R. & Aruldoss Albert Victoire T., 2018. "Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-35, May.
    12. Sharifian, Amir & Ghadi, M. Jabbari & Ghavidel, Sahand & Li, Li & Zhang, Jiangfeng, 2018. "A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data," Renewable Energy, Elsevier, vol. 120(C), pages 220-230.
    13. Hocaoglu, Fatih Onur & Karanfil, Fatih, 2013. "A time series-based approach for renewable energy modeling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 204-214.
    14. Costa, Marcelo Azevedo & Ruiz-Cárdenas, Ramiro & Mineti, Leandro Brioschi & Prates, Marcos Oliveira, 2021. "Dynamic time scan forecasting for multi-step wind speed prediction," Renewable Energy, Elsevier, vol. 177(C), pages 584-595.
    15. Samet, Haidar & Marzbani, Fatemeh, 2014. "Quantizing the deterministic nonlinearity in wind speed time series," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 1143-1154.
    16. Liu, Jinqiang & Wang, Xiaoru & Lu, Yun, 2017. "A novel hybrid methodology for short-term wind power forecasting based on adaptive neuro-fuzzy inference system," Renewable Energy, Elsevier, vol. 103(C), pages 620-629.
    17. Ramasamy, P. & Chandel, S.S. & Yadav, Amit Kumar, 2015. "Wind speed prediction in the mountainous region of India using an artificial neural network model," Renewable Energy, Elsevier, vol. 80(C), pages 338-347.
    18. Mohammadi, Kasra & Shamshirband, Shahaboddin & Yee, Por Lip & Petković, Dalibor & Zamani, Mazdak & Ch, Sudheer, 2015. "Predicting the wind power density based upon extreme learning machine," Energy, Elsevier, vol. 86(C), pages 232-239.
    19. Xie, Yuying & Li, Chaoshun & Tang, Geng & Liu, Fangjie, 2021. "A novel deep interval prediction model with adaptive interval construction strategy and automatic hyperparameter tuning for wind speed forecasting," Energy, Elsevier, vol. 216(C).
    20. Tascikaraoglu, A. & Uzunoglu, M., 2014. "A review of combined approaches for prediction of short-term wind speed and power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 243-254.
    21. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    22. Chang, G.W. & Lu, H.J. & Chang, Y.R. & Lee, Y.D., 2017. "An improved neural network-based approach for short-term wind speed and power forecast," Renewable Energy, Elsevier, vol. 105(C), pages 301-311.
    23. José Carlos Palomares-Salas & Agustín Agüera-Pérez & Juan José González de la Rosa & José María Sierra-Fernández & Antonio Moreno-Muñoz, 2013. "Exogenous Measurements from Basic Meteorological Stations for Wind Speed Forecasting," Energies, MDPI, vol. 6(11), pages 1-19, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tascikaraoglu, A. & Uzunoglu, M., 2014. "A review of combined approaches for prediction of short-term wind speed and power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 243-254.
    2. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    3. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    4. De Giorgi, Maria Grazia & Ficarella, Antonio & Tarantino, Marco, 2011. "Assessment of the benefits of numerical weather predictions in wind power forecasting based on statistical methods," Energy, Elsevier, vol. 36(7), pages 3968-3978.
    5. Yu, Jie & Chen, Kuilin & Mori, Junichi & Rashid, Mudassir M., 2013. "A Gaussian mixture copula model based localized Gaussian process regression approach for long-term wind speed prediction," Energy, Elsevier, vol. 61(C), pages 673-686.
    6. Chen, Kuilin & Yu, Jie, 2014. "Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach," Applied Energy, Elsevier, vol. 113(C), pages 690-705.
    7. José Carlos Palomares-Salas & Agustín Agüera-Pérez & Juan José González de la Rosa & José María Sierra-Fernández & Antonio Moreno-Muñoz, 2013. "Exogenous Measurements from Basic Meteorological Stations for Wind Speed Forecasting," Energies, MDPI, vol. 6(11), pages 1-19, November.
    8. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    9. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    10. Sandra Minerva Valdivia-Bautista & José Antonio Domínguez-Navarro & Marco Pérez-Cisneros & Carlos Jesahel Vega-Gómez & Beatriz Castillo-Téllez, 2023. "Artificial Intelligence in Wind Speed Forecasting: A Review," Energies, MDPI, vol. 16(5), pages 1-28, March.
    11. Drisya, G.V. & Asokan, K. & Kumar, K. Satheesh, 2018. "Diverse dynamical characteristics across the frequency spectrum of wind speed fluctuations," Renewable Energy, Elsevier, vol. 119(C), pages 540-550.
    12. Wang, Jian-Zhou & Wang, Yun & Jiang, Ping, 2015. "The study and application of a novel hybrid forecasting model – A case study of wind speed forecasting in China," Applied Energy, Elsevier, vol. 143(C), pages 472-488.
    13. Liu, Hui & Tian, Hong-qi & Liang, Xi-feng & Li, Yan-fei, 2015. "Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks," Applied Energy, Elsevier, vol. 157(C), pages 183-194.
    14. Wang, Jianzhou & Xiong, Shenghua, 2014. "A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China," Energy, Elsevier, vol. 76(C), pages 526-541.
    15. Erasmo Cadenas & Wilfrido Rivera & Rafael Campos-Amezcua & Christopher Heard, 2016. "Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model," Energies, MDPI, vol. 9(2), pages 1-15, February.
    16. Wang, Jianzhou & Qin, Shanshan & Zhou, Qingping & Jiang, Haiyan, 2015. "Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China," Renewable Energy, Elsevier, vol. 76(C), pages 91-101.
    17. Hoolohan, Victoria & Tomlin, Alison S. & Cockerill, Timothy, 2018. "Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data," Renewable Energy, Elsevier, vol. 126(C), pages 1043-1054.
    18. De Giorgi, Maria Grazia & Ficarella, Antonio & Tarantino, Marco, 2011. "Error analysis of short term wind power prediction models," Applied Energy, Elsevier, vol. 88(4), pages 1298-1311, April.
    19. Ziel, Florian & Croonenbroeck, Carsten & Ambach, Daniel, 2016. "Forecasting wind power – Modeling periodic and non-linear effects under conditional heteroscedasticity," Applied Energy, Elsevier, vol. 177(C), pages 285-297.
    20. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:rensus:v:16:y:2012:i:7:p:4563-4573. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.