Research Article Day-Ahead Wind Speed Forecasting Using Relevance Vector Machine Guoqiang Sun, 1 Yue Chen, 1 Zhinong Wei, 1 Xiaolu Li, 2 and Kwok W. Cheung 3 1 Research Center for Renewable Energy Generation Engineering, Ministry of Education, Hohai University, Nanjing 210098, China 2 ALSTOM GRID Technology Center Co., Ltd., Shanghai 201114, China 3 ALSTOM Grid Inc., Redmond, WA 98052, USA Correspondence should be addressed to Guoqiang Sun; hhusunguoqiang@163.com Received 31 December 2013; Revised 5 May 2014; Accepted 22 May 2014; Published 12 June 2014 Academic Editor: Hongjie Jia Copyright © 2014 Guoqiang Sun et al. his is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. With the development of wind power technology, the security of the power system, power quality, and stable operation will meet new challenges. So, in this paper, we propose a recently developed machine learning technique, relevance vector machine (RVM), for day-ahead wind speed forecasting. We combine Gaussian kernel function and polynomial kernel function to get mixed kernel for RVM. hen, RVM is compared with back propagation neural network (BP) and support vector machine (SVM) for wind speed forecasting in four seasons in precision and velocity; the forecast results demonstrate that the proposed method is reasonable and efective. 1. Introduction Over the past decade, people in many countries worldwide have paid signiicant attention to wind power generation because of it being pollution-free, clean, and renewable. At the end of 2012, worldwide installed capacity of wind power reached 282.2GW, increased almost 20% compared to the previous year 2011 which of 240 GW. By the end of 2020, total global installed capacity will reach 1150 GW, and wind power will be over 2800 TWh, accounting for about 12% of global electricity demand; by the end of 2030, installed capacity will exceed 2500GW, and wind power generating capacity will reach 6600 TWh, accounting for about 23% of global electricity demand [1]. he introduction of such a large-scale wind power has attracted many domestic and foreign scholars for further wind power technology. he wind forecast as a basic link of the wind power research is one of the efective ways to solve the problem and has an important role in the safe and economic operation of the power grid, so a growing number of researchers pay attention to it recently. We can cluster the wind forecasting techniques into two main groups; the irst group are physical methods, taking physical considerations into account, such as temperature and local terrain. In [2, 3], numerical weather prediction (NWP) model could be used directly for wind speed and wind energy predictions. Another group are statistical methods. Conventional ones are identical to the direct random time-series model, such as autoregressive model (AR), moving average model (MA), autoregressive moving average model (ARMA), and autoregressive integrated moving average model (ARIMA). Kamal and Jafri [4] established an ARMA model and found for long-term or short-term predictions, the values of vari- ances and wind speed with a conidence interval of 95% were acceptable. A fractional-ARIMA (f-ARIMA) model was used by Kavasseri and Seetharaman [5] for day-ahead and two-day-ahead wind speed forecasting. Results showed that forecast accuracy was signiicantly improved with f-ARIMA model compared to the previous method. Apart from the mentioned forecasting techniques, machine learning algorithms such as artiicial neural network (ANN), Bayesian network (BN), and support vector machine (SVM) are usually adopted for time series-based wind prediction. Bilgili et al. [6] investigated the use of a model based on the ANN method and spatial correlation for monthly wind speed prediction without any topographic details or other meteorological data. he prediction results showed that the maximum MAE was 14.13%, while Hindawi Publishing Corporation Journal of Applied Mathematics Volume 2014, Article ID 437592, 6 pages http://dx.doi.org/10.1155/2014/437592