메뉴 건너뛰기




Volumn 156, Issue , 2015, Pages 321-330

Short-term wind speed prediction based on robust Kalman filtering: An experimental comparison

Author keywords

Outliers; Robust Kalman filtering; Wind power generation; Wind speed prediction

Indexed keywords

ELECTRIC POWER GENERATION; FORECASTING; HIGHER ORDER STATISTICS; KALMAN FILTERS; SPEED; STATISTICAL METHODS; STATISTICS; WIND EFFECTS; WIND POWER;

EID: 84937875109     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2015.07.043     Document Type: Article
Times cited : (155)

References (47)
  • 1
    • 78650045524 scopus 로고    scopus 로고
    • A review of wind power and wind speed forecasting methods with different time horizons
    • North American Power Symposium (NAPS), 2010
    • Soman S, Zareipour H, Malik O. Mandal P. A review of wind power and wind speed forecasting methods with different time horizons. North American Power Symposium (NAPS), 2010; 2010, p. 1-8.
    • (2010) , pp. 1-8
    • Soman, S.1    Zareipour, H.2    Malik, O.3    Mandal, P.4
  • 2
    • 33646352206 scopus 로고    scopus 로고
    • Very short-term wind forecasting for Tasmanian power generation
    • Potter C.W., Negnevitsky M. Very short-term wind forecasting for Tasmanian power generation. IEEE Trans Power Syst 2006, 21(2):965-972.
    • (2006) IEEE Trans Power Syst , vol.21 , Issue.2 , pp. 965-972
    • Potter, C.W.1    Negnevitsky, M.2
  • 3
    • 67349212886 scopus 로고    scopus 로고
    • A comparison of the impact of quikscat and windsat wind vector products on met office analyses and forecasts
    • Candy B., English S.J., Keogh S.J. A comparison of the impact of quikscat and windsat wind vector products on met office analyses and forecasts. IEEE T Geosci Remote Sens 2009, 47(6):1632-1640.
    • (2009) IEEE T Geosci Remote Sens , vol.47 , Issue.6 , pp. 1632-1640
    • Candy, B.1    English, S.J.2    Keogh, S.J.3
  • 4
    • 70449339932 scopus 로고    scopus 로고
    • Wind speed prediction based on simple meteorological data using artificial neural network
    • INDIN 2009. 7th IEEE International Conference on
    • Ghanbarzadeh A, Noghrehabadi A, Behrang M, Assareh E. Wind speed prediction based on simple meteorological data using artificial neural network. In: Industrial Informatics, 2009. INDIN 2009. 7th IEEE International Conference on.; 2009. p. 664-7. doi:10.1109/INDIN.2009.5195882.
    • (2009) Industrial Informatics, 2009 , pp. 664-667
    • Ghanbarzadeh, A.1    Noghrehabadi, A.2    Behrang, M.3    Assareh, E.4
  • 5
    • 0035451837 scopus 로고    scopus 로고
    • Using neural networks to estimate wind turbine power generation
    • Shuhui L., Wunsch D., O'Hair E., Giesselmann M. Using neural networks to estimate wind turbine power generation. IEEE Trans Energy Convers 2001, 16(3):276-282. 10.1109/60.937208.
    • (2001) IEEE Trans Energy Convers , vol.16 , Issue.3 , pp. 276-282
    • Shuhui, L.1    Wunsch, D.2    O'Hair, E.3    Giesselmann, M.4
  • 7
    • 84859036543 scopus 로고    scopus 로고
    • AWNN-assisted wind power forecasting using feed-forward neural network
    • Bhaskar K., Singh S. AWNN-assisted wind power forecasting using feed-forward neural network. IEEE Trans Sustain Energy 2012, 3(2):306-315.
    • (2012) IEEE Trans Sustain Energy , vol.3 , Issue.2 , pp. 306-315
    • Bhaskar, K.1    Singh, S.2
  • 8
    • 79751505649 scopus 로고    scopus 로고
    • Bayesian adaptive combination of short-term wind speed forecasts from neural network models
    • Li G., Shi J., Zhou J. Bayesian adaptive combination of short-term wind speed forecasts from neural network models. Renew Energy 2011, 36(1):352-359. 10.1016/j.renene.2010.06.049.
    • (2011) Renew Energy , vol.36 , Issue.1 , pp. 352-359
    • Li, G.1    Shi, J.2    Zhou, J.3
  • 9
    • 0030241740 scopus 로고    scopus 로고
    • Time-series models for reliability evaluation of power systems including wind energy
    • Billinton R., Chen H., Ghajar R. Time-series models for reliability evaluation of power systems including wind energy. Microelectron Reliability 1996, 36(9):1253-1261. 10.1016/0026-2714(95)00154-9.
    • (1996) Microelectron Reliability , vol.36 , Issue.9 , pp. 1253-1261
    • Billinton, R.1    Chen, H.2    Ghajar, R.3
  • 10
    • 58949103845 scopus 로고    scopus 로고
    • Day-ahead wind speed forecasting using f-ARIMA models
    • Kavasseri R., Seetharaman K. Day-ahead wind speed forecasting using f-ARIMA models. Renew Energy 2009, 34(5):1388-1393.
    • (2009) Renew Energy , vol.34 , Issue.5 , pp. 1388-1393
    • Kavasseri, R.1    Seetharaman, K.2
  • 11
    • 78650562310 scopus 로고    scopus 로고
    • ARMA based approaches for forecasting the tuple of wind speed and direction
    • Erdem E., Shi J. ARMA based approaches for forecasting the tuple of wind speed and direction. Appl Energy 2011, 88(4):1405-1414.
    • (2011) Appl Energy , vol.88 , Issue.4 , pp. 1405-1414
    • Erdem, E.1    Shi, J.2
  • 12
    • 84860255082 scopus 로고    scopus 로고
    • Forecasting wind speed with recurrent neural networks
    • Cao Q., Ewing B.T., Thompson M.A. Forecasting wind speed with recurrent neural networks. Eur J Oper Res 2012, 221(1):148-154.
    • (2012) Eur J Oper Res , vol.221 , Issue.1 , pp. 148-154
    • Cao, Q.1    Ewing, B.T.2    Thompson, M.A.3
  • 13
    • 54149110925 scopus 로고    scopus 로고
    • A new strategy for wind speed forecasting using artificial intelligent methods
    • Monfared M., Rastegar H., Kojabadi H. A new strategy for wind speed forecasting using artificial intelligent methods. Renew Energy 2009, 34(3):845-848.
    • (2009) Renew Energy , vol.34 , Issue.3 , pp. 845-848
    • Monfared, M.1    Rastegar, H.2    Kojabadi, H.3
  • 14
    • 61649090211 scopus 로고    scopus 로고
    • Short-term prediction of wind farm power: a data mining approach
    • Kusiak A., Zheng H., Song Z. Short-term prediction of wind farm power: a data mining approach. IEEE Trans Energy Convers 2009, 24(1):125-136. 10.1109/TEC.2008.2006552.
    • (2009) IEEE Trans Energy Convers , vol.24 , Issue.1 , pp. 125-136
    • Kusiak, A.1    Zheng, H.2    Song, Z.3
  • 16
    • 79952454042 scopus 로고    scopus 로고
    • Multiple architecture system for wind speed prediction
    • Bouzgou H., Benoudjit N. Multiple architecture system for wind speed prediction. Appl Energy 2011, 88(7):2463-2471.
    • (2011) Appl Energy , vol.88 , Issue.7 , pp. 2463-2471
    • Bouzgou, H.1    Benoudjit, N.2
  • 17
    • 84859416828 scopus 로고    scopus 로고
    • Evaluation of hybrid forecasting approaches for wind speed and power generation time series
    • Shi J., Guo J., Zheng S. Evaluation of hybrid forecasting approaches for wind speed and power generation time series. Renew Sustain Energy Rev 2012, 16(5):3471-3480. 10.1016/ j.rser.2012.02.044.
    • (2012) Renew Sustain Energy Rev , vol.16 , Issue.5 , pp. 3471-3480
    • Shi, J.1    Guo, J.2    Zheng, S.3
  • 18
    • 84919784667 scopus 로고    scopus 로고
    • Raw wind data preprocessing: a data-mining approach
    • Zheng L., Hu W., Min Y. Raw wind data preprocessing: a data-mining approach. IEEE Trans Sustain Energy 2015, 6(1):11-19. 10.1109/TSTE.2014.2355837.
    • (2015) IEEE Trans Sustain Energy , vol.6 , Issue.1 , pp. 11-19
    • Zheng, L.1    Hu, W.2    Min, Y.3
  • 19
    • 84908476910 scopus 로고    scopus 로고
    • A hybrid forecasting model based on outlier detection and fuzzy time series a case study on Hainan wind farm of China
    • Wang J., Xiong S. A hybrid forecasting model based on outlier detection and fuzzy time series a case study on Hainan wind farm of China. Energy 2014, 76(0):526-541.
    • (2014) Energy , vol.76 , pp. 526-541
    • Wang, J.1    Xiong, S.2
  • 20
    • 84911940566 scopus 로고    scopus 로고
    • Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, 520 China
    • Wang J., Qin S., Zhou Q., Jiang H. Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, 520 China. Renew Energy 2015, 76(0):91-101.
    • (2015) Renew Energy , vol.76 , pp. 91-101
    • Wang, J.1    Qin, S.2    Zhou, Q.3    Jiang, H.4
  • 21
    • 78650838246 scopus 로고    scopus 로고
    • Cable-driven elastic parallel humanoid head with face tracking for autism spectrum disorder interventions
    • Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE
    • Su H, Dickstein-Fischer L, Harrington K, Fu Q, Lu W, Huang H, et al. Cable-driven elastic parallel humanoid head with face tracking for autism spectrum disorder interventions. In: Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 2010. p. 467-70. http://10.1109/IEMBS.2010.5626186.
    • (2010) , pp. 467-470
    • Su, H.1    Dickstein-Fischer, L.2    Harrington, K.3    Fu, Q.4    Lu, W.5    Huang, H.6
  • 22
    • 79955792667 scopus 로고    scopus 로고
    • Robust real-time multi-user pupil detection and tracking under various illumination and large-scale head motion
    • Yan C., Wang Y., Zhang Z. Robust real-time multi-user pupil detection and tracking under various illumination and large-scale head motion. Comput Vision Image Understand 2011, 115(8):1223-1238.
    • (2011) Comput Vision Image Understand , vol.115 , Issue.8 , pp. 1223-1238
    • Yan, C.1    Wang, Y.2    Zhang, Z.3
  • 24
    • 69849087908 scopus 로고    scopus 로고
    • Energy forward price prediction with a hybrid adaptive model
    • CIFEr '09. IEEE symposium on
    • Nguyen H, Nabney I. Energy forward price prediction with a hybrid adaptive model. In: Computational intelligence for financial engineering, 2009. CIFEr '09. IEEE symposium on.; 2009. p. 66-71. doi:10.1109/CIFER.2009.4937504.
    • (2009) Computational intelligence for financial engineering, 2009 , pp. 66-71
    • Nguyen, H.1    Nabney, I.2
  • 25
    • 49749138923 scopus 로고    scopus 로고
    • Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering
    • Louka P., Galanis G., Siebert N., Kariniotakis G., Katsafa-dos P., Pytharoulis I., et al. Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering. J Wind Eng Ind Aerodynam 2008, 96(12):2348-2362. 10.1016/j.jweia.2008.03.013.
    • (2008) J Wind Eng Ind Aerodynam , vol.96 , Issue.12 , pp. 2348-2362
    • Louka, P.1    Galanis, G.2    Siebert, N.3    Kariniotakis, G.4    Katsafa-dos, P.5    Pytharoulis, I.6
  • 26
    • 84864827118 scopus 로고    scopus 로고
    • Wind speed and wind energy forecast through Kalman filtering of numerical weather prediction model output
    • Cassola F., Burlando M. Wind speed and wind energy forecast through Kalman filtering of numerical weather prediction model output. Appl Energy 2012, 99(0):154-166. 10.1016/j.apenergy.2012. 03.054.
    • (2012) Appl Energy , vol.99 , pp. 154-166
    • Cassola, F.1    Burlando, M.2
  • 28
    • 84876303697 scopus 로고    scopus 로고
    • Automatic tuning of Kalman filters by maximum likelihood methods for wind energy forecasting
    • Poncela M., Poncela P., Pern J.R. Automatic tuning of Kalman filters by maximum likelihood methods for wind energy forecasting. Appl Energy 2013, 108(C):349-362.
    • (2013) Appl Energy , vol.108 , pp. 349-362
    • Poncela, M.1    Poncela, P.2    Pern, J.R.3
  • 29
    • 84862213628 scopus 로고    scopus 로고
    • Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction
    • Liu H., Qi Tian H., Fei Li Y. Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction. Appl Energy 2012, 98(0):415-424.
    • (2012) Appl Energy , vol.98 , pp. 415-424
    • Liu, H.1    Qi Tian, H.2    Fei Li, Y.3
  • 30
    • 84883355288 scopus 로고    scopus 로고
    • Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach
    • Chen K., Yu J. Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach. Appl Energy 2014, 113(0):690-705.
    • (2014) Appl Energy , vol.113 , pp. 690-705
    • Chen, K.1    Yu, J.2
  • 31
    • 51349118274 scopus 로고    scopus 로고
    • A Kalman filter for robust outlier detection
    • IROS 2007. IEEE/RSJ international conference on
    • Ting JA, Theodorou E, Schaal S. A Kalman filter for robust outlier detection. In: Intelligent robots and systems, 2007. IROS 2007. IEEE/RSJ international conference on.; 2007. p. 1514-9. http://10.1109/IROS.2007.4399158.
    • (2007) Intelligent robots and systems, 2007 , pp. 1514-1519
    • Ting, J.A.1    Theodorou, E.2    Schaal, S.3
  • 33
    • 21844510440 scopus 로고
    • Robust recursive estimation in the presence of heavy-tailed observation noise
    • Schick I.C., Mitter S.K. Robust recursive estimation in the presence of heavy-tailed observation noise. Ann Statist 1994, 22(2):1045-1080.
    • (1994) Ann Statist , vol.22 , Issue.2 , pp. 1045-1080
    • Schick, I.C.1    Mitter, S.K.2
  • 34
    • 0036903559 scopus 로고    scopus 로고
    • Wind models for simulation of power fluctuations from wind farms
    • Sørensen P., Hansen A.D., Rosas P.A.C. Wind models for simulation of power fluctuations from wind farms. J Wind Eng Ind Aerodynam 2002, 90(12-15):1381-1402.
    • (2002) J Wind Eng Ind Aerodynam , vol.90 , Issue.12-15 , pp. 1381-1402
    • Sørensen, P.1    Hansen, A.D.2    Rosas, P.A.C.3
  • 40
    • 84947791529 scopus 로고    scopus 로고
    • Wiley Series in Probability and Statistics, New York
    • Huber P. Robust statistics 2009, Wiley Series in Probability and Statistics, New York. 2nd ed.
    • (2009) Robust statistics
    • Huber, P.1
  • 41
    • 0003793619 scopus 로고
    • Robust Kalman filter 580 and its application in time series analysis
    • Cipra T., Romera R. Robust Kalman filter 580 and its application in time series analysis. Kybernetika 1991, 27(6):481-494.
    • (1991) Kybernetika , vol.27 , Issue.6 , pp. 481-494
    • Cipra, T.1    Romera, R.2
  • 45
    • 13444287831 scopus 로고    scopus 로고
    • ROBPCA: a new approach to robust principal component analysis
    • Hubert M., Rousseeuw P., Vanden ROBPCA: a new approach to robust principal component analysis. Technometrics 2005, 47:64-79.
    • (2005) Technometrics , vol.47 , pp. 64-79
    • Hubert, M.1    Rousseeuw, P.2    Vanden3
  • 46
    • 0035310763 scopus 로고    scopus 로고
    • Exploring process data
    • Pearson R. Exploring process data. J Process Control 2001, 11(2):179-194. 10.1016/S0959-1524(00)00046-9.
    • (2001) J Process Control , vol.11 , Issue.2 , pp. 179-194
    • Pearson, R.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.