基于关联规则的PSO-Elman短期风速预测

传统神经网络在短期风速预测中，存在易陷入局部极值和动态性能不足等问题，从而导致风速预测精度较低。为了提高风速预测精度，提出一种基于关联规则的粒子群优化Elman神经网络风速预测模型。利用粒子群算法优化Elman神经网络模型参数，以提高算法的收敛速度，避免陷入局部极值，以得到最优的预测值。同时结合关联规则分析考虑气象因素，采用Apriori算法对风速与其他气象因素进行关联规则挖掘，并利用得到的关联规则对风速预测值进行修正与补偿。实验结果表明，所提出的预测模型的预测效果比传统模型的效果更佳，同时验证了结合关联规则考虑气象因素能够降低风速预测误差。

Short-term wind speed forecasting based on PSO-Elman optimized by association rule

In the short-term forecasting of wind speed, the traditional neural network is easy to fall into local extreme value and lack of dynamic performance, which leads to low accuracy of wind speed forecasting. In order to improve the prediction accuracy of wind speed, the wind speed forecasting model based on association rules and particle swarm optimization Elman neural network is proposed. PSO is used to optimize the parameters of Elman neural network model, which can improve the convergence rate of the algorithm and avoid falling into local extreme value, so as to get the best predictive value. And according to association rule, the meteorological factors are incorporated in the relevant algorithm.The Apriori algorithm is used to mine the association rules of wind speed and other meteorological factors, and the wind speed forecasting value is modified and compensated by the association rules. The experimental results show that the forecast effect of the proposed model is better than the traditional model, and it is proved that the combination of the association rules considering the meteorological factors can reduce the wind speed prediction error.