This paper considers the effect of nonlinear atmospheric disturbances on wind power prediction. A Lorenz system is introduced as an atmospheric disturbance model. Three new improved wind forecasting models combined with a Lorenz comprehensive disturbance are put forward in this study. Firstly, we define the form of the Lorenz disturbance variable and the wind speed perturbation formula. Then, different artificial neural network models are used to verify the new idea and obtain better wind speed

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... predictions. Finally we separately use the original and improved wind speed series to predict the related wind power. This proves that the corrected wind speed provides higher precision wind power predictions. This research presents a totally new direction in the wind prediction field and has profound theoretical research value and practical guiding significance. Keywords: wind energy; wind speed and power prediction; Lorenz system; atmospheric disturbance; artificial neural network OPEN ACCESS Energies 2015, 8 476 Introduction New energy generally refers to unconventional energy sources, such as wind power, solar power, ocean energy, hydropower, biomass energy, geothermal energy, and so on. In recent years, the development and utilization of new energy has become one of the most important approaches to solve the strain on resources and environmental deterioration. Wind energy, which is clean, renewable, and widely distributed, can be effectively used for large-scale wind power generation. According to statistics from the Global Wind Energy Council (GWEC) [1], global installed wind power capacity had reached 318,117 MW by the end of 2013, which is six times as much as it was 10 years ago. Wind energy is one of the most crucial meteorological factors during wind farm operation [2,3]. The stochastic volatility and intermittent nature of wind energy make wind power possess similar instability. A wide range of wind power integrated into a power system would exert a significant influence on power quality and security. High-precision wind power prediction thus is an imperative for wind energy development. Lots of mature and stable wind power prediction systems have been developed by international scholars in recent years [4] [5] [6] . The most representative forecasting systems abroad include the Prediktor system from the Danish National Laboratory, the WPPT system of Technical University of Denmark, the eWind system in the United States, and the AWPPS system in France, etc. The typical prediction systems in China generally include the WINPOP system developed by the China Meteorological Administration, and the WPPS system developed by the Meteorological Service Center in Hubei Province, etc. According to the different modeling methods used the current wind power prediction models can be divided into physical models, statistical models, artificial intelligence, and hybrid models. Some physical and geographical factors, such as air temperature, atmospheric pressure, atmospheric density, topography and surface roughness, are applied in physical models to obtain wind speed at the axial fan hub. Thus high resolution numerical weather prediction is realized by this means, which is especially suitable for long-term wind power prediction [7] [8] [9] . Based on large amounts of historical data, statistical models, which generally include the persistence model (PM), time series model (TSM), and Kalman filtering model (KFM), are aimed at establishing a linear relationship between input and output of prediction models [8, 10, 11] . In recent years, artificial intelligent technology has been widely used in the field of wind power prediction. Artificial intelligence takes many forms such as wavelet neural network (WNN) [12] , error back propagation neural network (BP), radial basis function neural network (RBF), support vector machine (SVM) [13] , and fuzzy logic (FL) [14] . In order to avoid the limitations of individual forecasting methods, hybrid models are being increasingly proposed in recent years [15] [16] [17] . WNN, SVM and BP networks are used in this prediction research. The WNN model needs large amounts of historical data to obtain a good prediction result. The BP network, which is especially suitable for small sample wind power prediction, has fast convergence speed and satisfactory performance. SVM has stable predictive ability but low convergence speed. Based on the above three prediction models, the corresponding disturbance models are proposed in this study, which fully consider the nonlinear disturbance effects in the atmosphere system.