As a kind of clean energy, wind energy is widely disseminated and has been widely researched. Compared with other methods, the support vector machine algorithm is more logical. Least squares support vector machine can improve training efficiency. Therefore, the method of multi-output least squares support vector regression is used to forecast the wind speed and wind direction in this paper. The bat algorithm is simple in structure and easy to understand. It has been applied to solve optimisation problems with MSVR. Compared with single output support vector machines, multi-output support vector machine readily solves problems of complex structure. The simulation model is established to predict the value of wind speed and wind direction by using different algorithms. The simulation results show that the multi-output least squares support vector machines prediction model based on bat optimisation algorithm has better feasibility and effectiveness.

支持向量机回归是一种重要的机器学习算法,虽然已成功应用于多个领域,但针对复杂系统,单输出支持向量回归算法的训练时间过长并且缺乏实用性.多输出直觉模糊最小二乘支持向量回归(Intuitionistic Fuzzy Least Squares Support Vector Regression,IFLS-SVR)在多输出支持向量机的基础上引入了直觉模糊,解决了不确定多输出复杂系统问题,减少了训练时间.生活中复杂的多输出模型更为常见,文中在传统支持向量回归的基础上对其进行改进,提出多输出IFLS-SVR模型.多输出IFLS-SVR采用直觉模糊算法将实际数据转化为模糊数据,将二次规划优化问题转化为求解一系列线性方程组.与现有的模糊支持向量回归相比,多输出IFLS-SVR采用直觉模糊方法来计算隶属度函数,采用最小二乘法提高了算法的训练效率,减少了训练时间,获得了更精确的解.仿真结果表明,与其他方法相比,多输出IFLS-SVR取得了较好的效果.最后将多输出IFLS-SVR模型应用于复杂的风速风向预测,也取得了较好的效果.

Support vector regression (SVR) is an important machine learning algorithm, although some successful applications have been achieved. The algorithm for complex system is still worth studying. Multiple output intuitionistic fuzzy least squares support vector regression (IFLS-SVR) is improved by using the intuitionistic fuzzy to solve the problem of the uncertain multiple output complex system. Compared with the traditional fuzzy support vector regression, the model with the fuzzy membership and non-fuzzy membership is more close to the practical system. Multiple output IFLS-SVR transforms the actual data into fuzzy data and transforms the quadratic programming optimization problem into a series of linear equations. Compared with the current fuzzy support vector regression, multiple output IFLS-SVR in this paper adopted the intuitionistic fuzzy method to calculate membership functions, improving the training efficiency of the algorithm and reducing the training time by using the least square method. Through the simulation model, multiple output IFLS-SVR has achieved good results compared with other methods. The application of multiple output IFLS-SVR to the prediction of complex wind weather has also achieved good results.

For the Multi-output Support Vector Regression (MSVR) algorithm based on gradient descent method in the process of model parameter fitting,the convergence rate is slow and the prediction accuracy is low.A modified version of the Quasi-Newton algorithm (BFGS) with second-order convergence rate based on the rank-2 correction rule was used to fit the model parameters of MSVR algorithm.At the same time,to ensure the decrease of the iterative process and the global convergence,the step size factor was determined by the non-exact linear search technique.Based on the analysis of the geometry structure of kernel function in Support Vector Machine (SVM),a data-dependent kernel function was substituted for the traditional kernel function,and the multi-output data-dependent kernel support vector regression model was generated.The model was compared with the multi-output support vector regression model based on gradient descent method and modified Newton method.The experimental results show that in the case of 200 samples,the iterative time of the proposed algorithm is 72.98 s,the iterative time of modified Newton's algorithm is 116.34 s and the iterative time of gradient descent method is 2 065.22 s.The proposed algorithm can reduce the model iteration time and has faster convergence speed.