Abstract
It is well known that the inherent instability of wind speed may jeopardize the safety and operation of wind power generation, consequently affecting the power dispatch efficiency in power systems. Therefore, accurate short-term wind speed prediction can provide valuable information to solve the wind power grid connection problem. For this reason, the optimization of feedforward (FF) neural networks using an improved flower pollination algorithm is proposed. First of all, the empirical mode decomposition method is devoted to decompose the wind speed sequence into components of different frequencies for decreasing the volatility of the wind speed sequence. Secondly, a back propagation neural network is integrated with the improved flower pollination algorithm to predict the changing trend of each decomposed component. Finally, the predicted values of each component can get into an overlay combination process and achieve the purpose of accurate prediction of wind speed. Compared with major existing neural network models, the performance tests confirm that the average absolute error using the proposed algorithm can be reduced up to 3.67%.
Highlights
Natural energy sources like oil, coal and gas used for power generation are usually environmentally destructive in their harvesting to produce usable energy
In [17], the gray correlation analysis method connected with V-Support Vector Machine (V-support vector machine (SVM)) was applied for wind speed prediction
It reveals the predicted others.from ensemble empirical mode decomposition (EEMD)–improved flower pollination algorithm (IFPA)–FF model is relatively closer to the actual one than others
Summary
Natural energy sources like oil, coal and gas used for power generation are usually environmentally destructive in their harvesting to produce usable energy. Compared with the NWP model, this kind of model requires less computation, is faster and more convenient to use Statistical methods, such as time series, support vector machine (SVM) and artificial neural network (ANN), can present a simpler way than the physical methods, but the complexity process of wind speed generation was not fully considered [13]. In [17], the gray correlation analysis method connected with V-Support Vector Machine (V-SVM) was applied for wind speed prediction.
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