In recent years, managers and researchers have paid increasing attention to accurate and stable wind speed prediction due to its significant effect on power dispatching and power grid security. However, most previous research has focused only on enhancing either accuracy or stability, with few studies addressing the two issues, simultaneously. This task is challenging due to the intermittency and complex fluctuations of wind speed. Therefore, we proposed a novel hybrid system based on a newly proposed called the MOWOA, which includes four modules: a data preprocessing module, optimization module, forecasting module, and evaluation module. An effective decomposing technique is also applied to eliminate redundant noise and extract the primary characteristics of wind speed data. In order to obtain high accuracy, and stability for wind speed prediction simultaneously, and overcome the weaknesses of single objective optimization algorithms, the optimization module of the proposed MOWOA is utilized to optimize the weights and thresholds of the Elman neutral network used in the forecasting module. Finally, the evaluation module, which includes hypothesis testing, evaluation criteria, and three experiments, is introduced perform comprehensive evaluation on the system. The results indicate that the proposed MOWOA performs better than the two recently developed MOALO and MODA algorithms, and that the proposed hybrid model outperforms all sixteen models used for comparison, which demonstrates its superior ability to generate forecasts in terms of forecasting accuracy and stability.

近年来，由于风速预测对电力调度和电网安全具有重大影响，因此管理人员和研究人员越来越重视准确和稳定的风速预测。但是，大多数以前的研究仅集中于提高准确性或稳定性，很少有研究同时解决这两个问题。由于风速的间歇性和复杂的波动，这项任务具有挑战性。因此，我们基于新提出的MOWOA提出了一种新颖的混合系统，该系统包括四个模块：数据预处理模块，优化模块，预测模块和评估模块。一种有效的分解技术也被应用于消除冗余噪声并提取风速数据的主要特征。为了获得高精度，同时兼顾风速预测的稳定性和稳定性，并克服了单目标优化算法的缺点，利用提出的MOWOA的优化模块来优化预测模块中使用的Elman神经网络的权重和阈值。最后，引入了包括假设检验，评估标准和三个实验的评估模块，对系统进行了综合评估。结果表明，所提出的MOWOA的性能优于最近开发的两种MOALO和MODA算法，并且所提出的混合模型优于用于比较的所有16种模型，这表明了其在预测准确性和稳定性方面产生预测的卓越能力。为了克服单目标优化算法的不足，利用提出的MOWOA的优化模块来优化预测模块中使用的Elman神经网络的权重和阈值。最后，引入了包括假设检验，评估标准和三个实验的评估模块，对系统进行了综合评估。结果表明，所提出的MOWOA的性能优于最近开发的两种MOALO和MODA算法，并且所提出的混合模型优于用于比较的所有16种模型，这表明了其在预测准确性和稳定性方面产生预测的卓越能力。为了克服单目标优化算法的缺点，提出的MOWOA优化模块被用于优化预测模块中使用的Elman神经网络的权重和阈值。最后，引入了包括假设检验，评估标准和三个实验的评估模块，对系统进行了综合评估。结果表明，所提出的MOWOA的性能优于最近开发的两种MOALO和MODA算法，并且所提出的混合模型优于用于比较的所有16种模型，这表明了其在预测准确性和稳定性方面产生预测的卓越能力。利用提出的MOWOA的优化模块来优化预测模块中使用的Elman中性网络的权重和阈值。最后，引入了包括假设检验，评估标准和三个实验的评估模块，对系统进行了综合评估。结果表明，所提出的MOWOA的性能优于最近开发的两种MOALO和MODA算法，并且所提出的混合模型优于用于比较的所有16种模型，这表明了其在预测准确性和稳定性方面产生预测的卓越能力。提出的MOWOA的优化模块用于优化预测模块中使用的Elman中性网络的权重和阈值。最后，引入了包括假设检验，评估标准和三个实验的评估模块，对系统进行了综合评估。结果表明，所提出的MOWOA的性能优于最近开发的两种MOALO和MODA算法，并且所提出的混合模型优于用于比较的所有16种模型，这表明了其在预测准确性和稳定性方面产生预测的卓越能力。