메뉴 건너뛰기




Volumn 27, Issue 8, 2016, Pages 2417-2428

Wind speed forecasting using the NARX model, case: La Mata, Oaxaca, México

Author keywords

Granger test; Mahalanobis distance; NARX; Wind speed forecasting

Indexed keywords

BALLOONS; ERRORS; FORECASTING; MEAN SQUARE ERROR; METEOROLOGY; MULTIVARIABLE SYSTEMS; SOLAR RADIATION; SPEED;

EID: 84939825549     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-015-2012-y     Document Type: Article
Times cited : (55)

References (31)
  • 3
    • 84989245793 scopus 로고    scopus 로고
    • Reglas de despacho y operación del sistema eléctrico nacional
    • Centro Nacional de Control de Energía (CENACE) (2001) Reglas de despacho y operación del sistema eléctrico nacional, México
    • (2001) México
  • 4
    • 51849142610 scopus 로고    scopus 로고
    • Short term wind speed forecasting in La Venta, Oaxaca, Mexico, using artificial neural networks
    • Cadenas E, Rivera W (2009) Short term wind speed forecasting in La Venta, Oaxaca, Mexico, using artificial neural networks. Renew Energy 34:274–278
    • (2009) Renew Energy , vol.34 , pp. 274-278
    • Cadenas, E.1    Rivera, W.2
  • 6
    • 73549100861 scopus 로고    scopus 로고
    • Analysis and forecasting of wind velocity in Chetumal, Quintana Roo, using the single exponential smoothing method
    • Cadenas E, Jaramillo OA, Rivera W (2010) Analysis and forecasting of wind velocity in Chetumal, Quintana Roo, using the single exponential smoothing method. Renew Energy 35:925–930
    • (2010) Renew Energy , vol.35 , pp. 925-930
    • Cadenas, E.1    Jaramillo, O.A.2    Rivera, W.3
  • 7
    • 77953137822 scopus 로고    scopus 로고
    • On comparing three artificial neural networks for wind speed forecasting
    • Li G, Shi J (2010) On comparing three artificial neural networks for wind speed forecasting. Appl Energy 87:2313–2320
    • (2010) Appl Energy , vol.87 , pp. 2313-2320
    • Li, G.1    Shi, J.2
  • 8
    • 77949570119 scopus 로고    scopus 로고
    • A hybrid statistical method to predict wind speed and wind power
    • Liu H, Tian H, Chen C, Li Y (2010) A hybrid statistical method to predict wind speed and wind power. Renew Energy 35:1857–1861
    • (2010) Renew Energy , vol.35 , pp. 1857-1861
    • Liu, H.1    Tian, H.2    Chen, C.3    Li, Y.4
  • 9
    • 77954315872 scopus 로고    scopus 로고
    • Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model
    • Cadenas E, Rivera W (2010) Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model. Renew Energy 35:2732–2738
    • (2010) Renew Energy , vol.35 , pp. 2732-2738
    • Cadenas, E.1    Rivera, W.2
  • 10
    • 79751505649 scopus 로고    scopus 로고
    • Bayesian adaptive combination of short-term wind speed forecasts from neural network models
    • 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
    • (2011) Renew Energy , vol.36 , pp. 352-359
    • Li, G.1    Shi, J.2    Zhou, J.3
  • 11
    • 78149358777 scopus 로고    scopus 로고
    • Comprehensive evaluation of ARMA–GARCH(-M) approaches for modeling the mean and volatility of wind speed
    • Liu H, Erdem E, Shi J (2011) Comprehensive evaluation of ARMA–GARCH(-M) approaches for modeling the mean and volatility of wind speed. Appl Energy 88:724–732
    • (2011) Appl Energy , vol.88 , pp. 724-732
    • Liu, H.1    Erdem, E.2    Shi, J.3
  • 12
    • 79961127156 scopus 로고    scopus 로고
    • Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model
    • Guo Z, Zhao W, Lu H, Wang J (2012) Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model. Renew Energy 37:241–249
    • (2012) Renew Energy , vol.37 , pp. 241-249
    • Guo, Z.1    Zhao, W.2    Lu, H.3    Wang, J.4
  • 13
    • 84862213628 scopus 로고    scopus 로고
    • Comparison of two new ARIMA–ANN and ARIMA–Kalman hybrid methods for wind speed prediction
    • Liu H, Tian H, Li Y (2012) Comparison of two new ARIMA–ANN and ARIMA–Kalman hybrid methods for wind speed prediction. Appl Energy 98:415–424
    • (2012) Appl Energy , vol.98 , pp. 415-424
    • Liu, H.1    Tian, H.2    Li, Y.3
  • 14
    • 84864827118 scopus 로고    scopus 로고
    • Wind speed and wind energy forecast through Kalman filtering of numerical weather prediction model output
    • 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
    • (2012) Appl Energy , vol.99 , pp. 154-166
    • Cassola, F.1    Burlando, M.2
  • 15
    • 84864797603 scopus 로고    scopus 로고
    • Performance analysis of four modified approaches for wind speed forecasting
    • Zhang W, Wu J, Wang J, Zhao W, Shen L (2012) Performance analysis of four modified approaches for wind speed forecasting. Appl Energy 99:324–333
    • (2012) Appl Energy , vol.99 , pp. 324-333
    • Zhang, W.1    Wu, J.2    Wang, J.3    Zhao, W.4    Shen, L.5
  • 16
    • 84863508830 scopus 로고    scopus 로고
    • A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks
    • Liu H, Chen C, Tian H, Li Y (2012) A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks. Renew Energy 48:545–556
    • (2012) Renew Energy , vol.48 , pp. 545-556
    • Liu, H.1    Chen, C.2    Tian, H.3    Li, Y.4
  • 17
    • 84865439917 scopus 로고    scopus 로고
    • A hybrid strategy of short term wind power prediction
    • Peng H, Liu F, Yang X (2013) A hybrid strategy of short term wind power prediction. Renew Energy 50:590–595
    • (2013) Renew Energy , vol.50 , pp. 590-595
    • Peng, H.1    Liu, F.2    Yang, X.3
  • 18
    • 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 (2014) Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach. Appl Energy 113:690–705
    • (2014) Appl Energy , vol.113 , pp. 690-705
    • Chen, K.1    Yu, J.2
  • 19
    • 77953358593 scopus 로고    scopus 로고
    • A novel wind speed modeling approach using atmospheric pressure observations and hidden Markov models
    • Hocaoğlu FO, Gerek ON, Kurban M (2010) A novel wind speed modeling approach using atmospheric pressure observations and hidden Markov models. J Wind Eng Ind Aerodyn 98:472–481
    • (2010) J Wind Eng Ind Aerodyn , vol.98 , pp. 472-481
    • Hocaoğlu, F.O.1    Gerek, O.N.2    Kurban, M.3
  • 22
    • 42249097152 scopus 로고    scopus 로고
    • Identification of multivariate outliers: a performance study
    • Filzmoser P (2005) Identification of multivariate outliers: a performance study. Aust J Stat 34:127–138
    • (2005) Aust J Stat , vol.34 , pp. 127-138
    • Filzmoser, P.1
  • 23
    • 84883083678 scopus 로고    scopus 로고
    • A time series-based approach for renewable energy modeling
    • Hocaoğlu FO, Karanfil F (2013) A time series-based approach for renewable energy modeling. Renew Sustain Energy Rev 28:214
    • (2013) Renew Sustain Energy Rev , vol.28 , pp. 214
    • Hocaoğlu, F.O.1    Karanfil, F.2
  • 24
    • 84986174723 scopus 로고
    • Likelihood ratio statistics for autoregressive processes
    • Dickey DA, Fuller WA (1981) Likelihood ratio statistics for autoregressive processes. Econometrica 49:1057–1072
    • (1981) Econometrica , vol.49 , pp. 1057-1072
    • Dickey, D.A.1    Fuller, W.A.2
  • 25
    • 11244305511 scopus 로고    scopus 로고
    • NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches
    • Gao Y, Er MJ (2005) NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches. Fuzzy Sets Syst 150(2):331–350
    • (2005) Fuzzy Sets Syst , vol.150 , Issue.2 , pp. 331-350
    • Gao, Y.1    Er, M.J.2
  • 26
    • 67549122417 scopus 로고    scopus 로고
    • Neural networks: a comprehensive foundation by Simon Haykin
    • Kubat M (1999) Neural networks: a comprehensive foundation by Simon Haykin. Knowl Eng Rev 13:409–412
    • (1999) Knowl Eng Rev , vol.13 , pp. 409-412
    • Kubat, M.1
  • 27
    • 0032146239 scopus 로고    scopus 로고
    • Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences
    • Gardner M, Dorling S (1998) Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos Environ 32:2627–2636
    • (1998) Atmos Environ , vol.32 , pp. 2627-2636
    • Gardner, M.1    Dorling, S.2
  • 28
    • 77950894862 scopus 로고    scopus 로고
    • The use of NARX neural networks to predict chaotic time series
    • Diaconescu E (2008) The use of NARX neural networks to predict chaotic time series. WSEAS Trans Comput Res 3(3):182–191
    • (2008) WSEAS Trans Comput Res , vol.3 , Issue.3 , pp. 182-191
    • Diaconescu, E.1
  • 29
    • 10244235219 scopus 로고    scopus 로고
    • An activation function adapting training algorithm for sigmoidal feedforward networks
    • Pravin C, Yogesh S (2004) An activation function adapting training algorithm for sigmoidal feedforward networks. Neurocomputing 61:429–437
    • (2004) Neurocomputing , vol.61 , pp. 429-437
    • Pravin, C.1    Yogesh, S.2
  • 31
    • 16444364474 scopus 로고    scopus 로고
    • Data division for developing neural networks applied to geotechnical engineering
    • Shahin MA, Maier HR, Jaksa MB (2004) Data division for developing neural networks applied to geotechnical engineering. J Comput Civil Eng ASCE 18(2):105–114
    • (2004) J Comput Civil Eng ASCE , vol.18 , Issue.2 , pp. 105-114
    • Shahin, M.A.1    Maier, H.R.2    Jaksa, M.B.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.