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Due to the non-linear and non-stationary characteristics of the wind speed time series, it is generally difficult to model and predict such series by single forecasting models. In this paper, two novel hybrid models, which combine empirical mode decomposition (EMD), feature selection with artificial neural network (ANN) and support vector machine (SVM), are proposed for short-term wind speed prediction. First, the original wind speed time series is decomposed into a set of sub-series by EMD. Second, the initial features (input variables) and targets are constructed from all the sub-series and the original series. Then, a feature selection process is introduced to constitute the relevant and informative features. Finally, a predictive model (ANN or SVM) is established using these selected features. The effectiveness of the proposed models has been assessed on the real datasets recorded from three wind farms in China. Compared with the single ANN, SVM, traditional EMD-based ANN, and traditional EMD-based SVM, the experimental results show that the proposed models have satisfactory performance, which are suitable for the wind speed prediction. [ABSTRACT FROM AUTHOR]

Photovoltaic generation of electricity is an important renewable energy source, and large numbers of relatively small photovoltaic systems are proliferating around the world. Today it is widely acknowledged by power producers, utility companies and independent system operators that it is only through advanced forecasting, communications and control that these distributed resources can collectively provide a firm, dispatchable generation capacity to the electricity market. One of the challenges of realizing such a goal is the precise forecasting of the output of individual photovoltaic systems, which is affected by a lot of factors. This paper introduces our short-term solar irradiance forecasting algorithms based on machine learning methodologies, Hidden Markov Model and SVM regression. A series of experimental evaluations are presented to analyze the relative performance of the techniques in order to show the importance of these methodologies. The Matlab interface, the Weather Forecasting Platform, has been used for these evaluations. The experiments are performed using the dataset generated by Australian Bureau of Meteorology. The experimental results show that our machine learning based forecasting algorithms can precisely predict future 5–30 min solar irradiance under different weather conditions. [ABSTRACT FROM AUTHOR]