Abstract
Wind speed forecasting plays a key role in wind-related engineering studies and is important in the management of wind farms. Current forecasting models based on different optimization algorithms can be adapted to various wind speed time series data. However, these methodologies cannot aggregate different hybrid forecasting methods and take advantage of the component models. To avoid these limitations, we propose a novel combined forecasting model called SSA-PSO-DWCM, i.e., particle swarm optimization (PSO) determined weight coefficients model. This model consisted of three main steps: one is the decomposition of the original wind speed signals to discard the noise, the second is the parameter optimization of the forecasting method, and the last is the combination of different models in a nonlinear way. The proposed combined model is examined by forecasting the wind speed (10-min intervals) of wind turbine 5 located in the Penglai region of China. The simulations reveal that the proposed combined model demonstrates a more reliable forecast than the component forecasting engines and the traditional combined method, which is based on a linear method.
Highlights
Due to increasing energy demands and environmental concerns, wind power has attracted global attention as a source of sustainable energy
The proposed Singular Spectrum Analysis (SSA)-Particle Swarm Optimization (PSO)-DWCM combined model was tested by forecasting the wind speed of wind turbine 5 located in the Penglai region of China
The evaluation index results for different forecasting methods are compared in Tables 7–10; the first six rows of these four tables present the forecasts without decomposition
Summary
Due to increasing energy demands and environmental concerns, wind power has attracted global attention as a source of sustainable energy. It is well known that wind energy has three main weaknesses; low density, instability and regional variations. These features make wind speed difficult to predict. Much research has been conducted to enhance wind speed forecasting accuracy, and these approaches can be divided into four categories: physical methods, statistical methods, hybrid physical-statistical approaches and artificial intelligence techniques [3]. Among these four categories, artificial intelligence techniques and statistical methods are the main methods studied in this paper
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