Short-Term Wind Speed Hybrid Forecasting Model Based on Bias Correcting Study and Its Application

Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2015, Article ID 351354, 13 pages http://dx.doi.org/10.1155/2015/351354 Research Article Short-Term Wind Speed Hybrid Forecasting Model Based on Bias Correcting Study and Its Application Mingfei Niu, Shaolong Sun, Jie Wu, and Yuanlei Zhang School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China Correspondence should be addressed to Shaolong Sun; Received 27 October 2014; Revised 7 December 2014; Accepted 8 December 2014 Academic Editor: Pandian Vasant Copyright © 2015 Mingfei Niu et al. This 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. The accuracy of wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. In particular, reliable short-term wind speed forecasting can enable model predictive control of wind turbines and realtime optimization of wind farm operation. However, due to the strong stochastic nature and dynamic uncertainty of wind speed, the forecasting of wind speed data using different patterns is difficult. This paper proposes a novel combination bias correcting forecasting method, which includes the combination forecasting method and forecasting bias correcting model. The forecasting result shows that the combination bias correcting forecasting method can more accurately forecast the trend of wind speed and has a good robustness. 1. Introduction Because of the global energy shortage, renewable energy has received increasing attention, just now. Wind power is one of the cleanest renewable energy sources that produces no greenhouse gases, has no effect on climate change, and produces little environmental impacts, and the energy generated from the wind has been well recognized as environmentally friendly, socially beneficial, and economically competitive for many applications [1]. As of now the effectiveness of wind speed forecasting is an important role in the scheduling of wind power. At present, these methods can be divided into two categories: statistical models and machine-learning models. Statistical models primarily use a time series approach and have been successfully applied for forecasting [2–7]. These models are based on the assumption that a linear correlation structure exists among time series values. Therefore, nonlinear patterns cannot be captured using these models. To overcome this limitation, machinelearning models have been used to improve nonlinear time series predictions (which primarily include artificial neural networks, support vector machines, heuristic algorithm, and fuzzy logic methods) [8–34]. In a nutshell, in the past decades, many computational intelligence techniques have been developed for short-term wind speed forecasting, for instance, support vector regression [15, 27, 35], support vector machine [26, 31, 33, 36], fuzzy model [21, 27], artificial neural networks [8, 9, 11, 13, 14, 20, 23–25, 28, 29], wavelet method [9, 34, 37], and heuristic intelligence algorithm: particle swarm optimization [37, 38], adaptive particle swarm optimization [17, 36, 39], chaotic particle swarm optimization [31], biogeography-based optimization [10], coral reefs optimization [16], gravitational search algorithm [19], and harmony search algorithm [16]. In the next section, we will detail the explanation of the previous work in the short-term wind speed prediction. The remaining sections are arranged as follows. The related work will be brief description in Section 2. The preparation methods and main modeling process are described from Section 3 to Section 6. Section 7 forecasts the wind speed of Penglai using three wind farms and provides the forecasting results and analyses. Finally, the conclusion is presented in Section 8 and the future research in Section 9. 2. Related Work In the above references, Song et al. [5] employ a discrete-state Markov chain to model the nonlinear characteristic of the wind speed time series, and a Bayesian inference is applied 2 to evaluate the parameters of the Markov-switching model. Finally, by comparison with other methods, this proposed method outperforms them. Liu et al. [9] present four important decomposing algorithms including wavelet decomposition, wavelet packet decomposition, empirical mode decomposition, and fast ensemble empirical mode decomposition, which are all adopted to realize the wind speed high-precision predictions. Salcedo-Sanz et al. [16] introduce a new hybrid coral reefs optimization and harmony search algorithm; this novel approach is utilized to obtain the best set of meteorological variables in the context of short-term wind speed forecasting, and the selection variable will be input to an extreme learning machine network. Experimental result shows that these proposed methods have good results when compared to other approaches. Wang et al. [17] proposed an optimization model to decide the rated power system and the capacity of a compressed air energy storage system in a power system with high wind power penetration. Moreno et al. [18] proposed a strategy including the uncertainty of involving market and wind power. Mondal et al. [19] solved economic dispatch problem in wind generation. In a nutshell, as the randomness of wind speed distribution, every prediction model owns some limitations. In the short-term wind speed forecasting, because of ignoring of the secondary influence factors and correlations, every prediction model can generate prediction errors, which are the difference between the predicted value and the actual value, the main causes that forecasting method just considers the main factors, and many of the secondary factors are ignored. However, as the effect of the secondary factors, the forecasting bias may form a certain trend. Making allowance for these minor influence factors, the bias correction becomes important. The basic idea of forecasting bias correction is following. After forecasting by the prediction model and comparing with the actual wind speed, forecasting error is generated. Using suitable prediction model to forecast error, error correction can be got, which is used to modify the original forecasting result. The error correction prediction model expression is as follows: 𝑌 = 𝑌𝑐 + 𝑒̂𝑐 . 𝑌 is final forecasting value, 𝑌𝑐 is combination forecasting value, and 𝑒̂𝑐 is bias correction. Nowadays, there are some error correction models [36, 40–42], such as the periodic extrapolation, vector error correction model, partial simulation approximate value, and Bayesian error correction model. But the relevant researches about the short-term wind speed and wind power error correction models are very rare. So this paper quotes bias correction model in short-term wind speed forecasting, thus, making wind power scheduling reasonable. Due to the ra (...truncated)