Forecasting models have been widely used in wind-speed time series forecasting that are often nonlinear, irregular, and non-stationary. Current forecasting models based on artificial neural network can adapt to various wind-speed time series. However, they cannot simultaneously and effectively forecast the entire wind-speed time series of a wind farm. In this paper, a novel combined forecasting system is developed for a wind farm that includes that SSAWD secondary de-noising algorithm is used to pre-process original wind speed data, and then the sub-model selection strategy is used to select five optimal sub models for the combined model. Meanwhile, a modified multi-objective optimization algorithm optimizes weight of the combined model, and the experimental results show that this forecasting system outperforms other traditional systems and can be effectively used to forecast wind-speed time series of a large wind farm.

预测模型已广泛用于风速时间序列预测中，通常是非线性，不规则和非平稳的。基于人工神经网络的当前预测模型可以适应各种风速时间序列。但是，他们无法同时有效地预测风电场的整个风速时间序列。本文开发了一种新颖的风电场联合预报系统，该系统包括使用SSAWD二次去噪算法对原始风速数据进行预处理，然后使用子模型选择策略选择五个最优子组合模型的模型。同时，改进的多目标优化算法可优化组合模型的权重，