Analysis and forecasting of wind velocity in chetumal, quintana roo, using the single exponential smoothing method

被引:79
作者
Cadenas, E. [2 ]
Jaramillo, O. A. [1 ]
Rivera, W. [1 ]
机构
[1] Univ Nacl Autonoma Mexico, Ctr Ivestigac Energia, Temixco 62580, Morelos, Mexico
[2] Univ Michoacana, Fac Ingn Mecan, Morelia, Michoacan, Mexico
关键词
Wind speed forecasting; Exponential smoothing method; ANN; ARMA; ARIMA; Hybrid systems; NEURAL-NETWORKS; SPEED; PREDICTION; ANN;
D O I
10.1016/j.renene.2009.10.037
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In this paper the analysis and forecasting of wind velocities in Chetumal, Quintana Roo, Mexico is presented. Measurements were made by the Instituto de Investigaciones Electricas (IIE) during two years, from 2004 to 2005. This location exemplifies the wind energy generation potential in the Caribbean coast of Mexico that could be employed in the hotel industry in the next decade. The wind speed and wind direction were measured at 10 m above ground level. Sensors with high accuracy and a low starting threshold were used. The wind velocity was recorded using a data acquisition system supplied by a 10 W photovoltaic panel. The wind speed values were measured with a frequency of 1 Hz and the average wind speed was recorded considering regular intervals of 10 min. First a statistical analysis of the time series was made in the first part of the paper through conventional and robust measures. Also the forecasting of the last day of measurements was made utilizing the single exponential smoothing method (SES). The results showed a very good accuracy of the data with this technique for an x value of 0.9. Finally the SES method was compared with the artificial neural network (ANN) method showing the former better results. (C) 2009 Elsevier Ltd. All rights reserved.
引用
收藏
页码:925 / 930
页数:6
相关论文
共 21 条
[1]   Short-term forecasting of wind speed and related electrical power [J].
Alexiadis, MC ;
Dikopoulos, PS ;
Sahsamanoglou, HS ;
Manousaridis, IM .
SOLAR ENERGY, 1998, 63 (01) :61-68
[2]   Application of artificial neural networks for the wind speed prediction of target station using reference stations data [J].
Bilgili, Mehmet ;
Sahin, Besir ;
Yasar, Abdulkadir .
RENEWABLE ENERGY, 2007, 32 (14) :2350-2360
[3]   Wind speed forecasting in the South Coast of Oaxaca, Mexico [J].
Cadenas, Erasmo ;
Rivera, Wilfrido .
RENEWABLE ENERGY, 2007, 32 (12) :2116-2128
[4]   Short-term ANN load forecasting from limited data using generalization learning strategies [J].
Chan, Zeke S. H. ;
Ngan, H. W. ;
Rad, A. B. ;
David, A. K. ;
Kasabov, N. .
NEUROCOMPUTING, 2006, 70 (1-3) :409-419
[5]   Time-series forecasting using flexible neural tree model [J].
Chen, YH ;
Yang, B ;
Dong, JW ;
Abraham, A .
INFORMATION SCIENCES, 2005, 174 (3-4) :219-235
[6]   A review on the young history of the wind power short-term prediction [J].
Costa, Alexandre ;
Crespo, Antonio ;
Navarro, Jorge ;
Lizcano, Gil ;
Madsen, Henrik ;
Feitosa, Everaldo .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2008, 12 (06) :1725-1744
[7]   Exponential smoothing in the telecommunications data [J].
Gardner, Everette S., Jr. ;
Diaz-Saiz, Joaquin .
INTERNATIONAL JOURNAL OF FORECASTING, 2008, 24 (01) :170-174
[8]   Exponential smoothing: The state of the art - Part II [J].
Gardner, Everette S., Jr. .
INTERNATIONAL JOURNAL OF FORECASTING, 2006, 22 (04) :637-666
[9]   A dynamic artificial neural network model for forecasting time series events [J].
Ghiassi, M ;
Saidane, H ;
Zimbra, DK .
INTERNATIONAL JOURNAL OF FORECASTING, 2005, 21 (02) :341-362
[10]   Wind power potential of Baja California Sur, Mexico [J].
Jaramillo, OA ;
Saldaña, R ;
Miranda, U .
RENEWABLE ENERGY, 2004, 29 (13) :2087-2100