基于小波变换和改进萤火虫算法优化LSSVM的短期风速预测

准确预测风速对风电规模化并网至关重要。为提高短期风速预测精度，提出一种基于小波分解和改进的萤火虫算法优化最小二乘支持向量机超参数的风速预测模型。首先利用小波变换将风速时序分解为近似序列和细节序列，然后对各序列分别利用一种新颖的混沌萤火虫算法优化LSSVM进行预测，最后将各序列预测值叠加得到最终风速预测值。在两种时间尺度的实测数据上进行仿真计算。结果表明，该算法较交叉验证的LSSVM, IPSO-LSSVM, WD-DE-LSSVM及BP神经网络等多种经典算法预测精度更高，表明了该算法的有效性和优越性。

Short-term wind speed forecasting based on WD-CFA-LSSVM model

Accurately predicting wind speed is of key importance for large scale wind power connecting to the grid. To improve the short-term wind speed forecasting accuracy, a least squares support vector machine wind speed prediction model based on wavelet decomposition and improved firefly algorithm is proposed. Firstly, the actual wind speed series is decomposed and reconstructed to approximate series and detail series, then the series are separately predicted by LSSVM optimized by chaotic firefly algorithm, at last the separate prediction series are superposed as the ultimate prediction wind speed. To verify the proposed model, two different time scale actual wind speed data are applied to simulation. The results show that the proposed model has higher prediction accuracy than classical model like CV-LSSVM, IPSO-LSSVM, WD-DE-LSSVM and BP neural networks, showing its validity and superiority.