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智能优化算法 神经网络预测 雷达通信 无线传感器
信号处理 图像处理 路径规划 元胞自动机 无人机 电力系统
⛄ 内容介绍
准确预测光伏电站输出功率,是促进光伏并网发电,提高电网运行稳定性的主要途径之一.该文提出一种基于粒子群算法最小二乘支持向量机(particle swarm optimization and least squares support vector machine,PSO-LSSVM)的日前光伏功率预测方法,该方法首先利用粒子群算法的全局搜索能力来获取最小二乘支持向量机的惩罚因子和核函数宽度,有效解决了最小二乘支持向量机难以快速精准寻找最优参数的问题;然后利用数值天气预报和光伏功率的历史数据对PSO-LSSVM模型进行训练,利用训练好的PSO-LSSVM模型对日前光伏功率进行预测.
⛄ 部分代码
function omega = kernel_matrix(Xtrain,kernel_type, kernel_pars,Xt)
% Construct the positive (semi-) definite and symmetric kernel matrix
%
% >> Omega = kernel_matrix(X, kernel_fct, sig2)
%
% This matrix should be positive definite if the kernel function
% satisfies the Mercer condition. Construct the kernel values for
% all test data points in the rows of Xt, relative to the points of X.
%
% >> Omega_Xt = kernel_matrix(X, kernel_fct, sig2, Xt)
%
%
% Full syntax
%
% >> Omega = kernel_matrix(X, kernel_fct, sig2)
% >> Omega = kernel_matrix(X, kernel_fct, sig2, Xt)
%
% Outputs
% Omega : N x N (N x Nt) kernel matrix
% Inputs
% X : N x d matrix with the inputs of the training data
% kernel : Kernel type (by default 'RBF_kernel')
% sig2 : Kernel parameter (bandwidth in the case of the 'RBF_kernel')
% Xt(*) : Nt x d matrix with the inputs of the test data
%
% See also:
% RBF_kernel, lin_kernel, kpca, trainlssvm, kentropy
% Copyright (c) 2011, KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.be/sista/lssvmlab
[nb_data,d] = size(Xtrain);
if strcmp(kernel_type,'RBF_kernel'),
if nargin<4,
XXh = sum(Xtrain.^2,2)*ones(1,nb_data);
omega = XXh+XXh'-2*(Xtrain*Xtrain');
omega = exp(-omega./(2*kernel_pars(1)));
else
XXh1 = sum(Xtrain.^2,2)*ones(1,size(Xt,1));
XXh2 = sum(Xt.^2,2)*ones(1,nb_data);
omega = XXh1+XXh2' - 2*Xtrain*Xt';
omega = exp(-omega./(2*kernel_pars(1)));
end
elseif strcmp(kernel_type,'RBF4_kernel'),
if nargin<4,
XXh = sum(Xtrain.^2,2)*ones(1,nb_data);
omega = XXh+XXh'-2*(Xtrain*Xtrain');
omega = 0.5*(3-omega./kernel_pars).*exp(-omega./(2*kernel_pars(1)));
else
XXh1 = sum(Xtrain.^2,2)*ones(1,size(Xt,1));
XXh2 = sum(Xt.^2,2)*ones(1,nb_data);
omega = XXh1+XXh2' - 2*Xtrain*Xt';
omega = 0.5*(3-omega./kernel_pars).*exp(-omega./(2*kernel_pars(1)));
end
% elseif strcmp(kernel_type,'sinc_kernel'),
% if nargin<4,
% omega = sum(Xtrain,2)*ones(1,size(Xtrain,1));
% omega = omega - omega';
% omega = sinc(omega./kernel_pars(1));
% else
% XXh1 = sum(Xtrain,2)*ones(1,size(Xt,1));
% XXh2 = sum(Xt,2)*ones(1,nb_data);
% omega = XXh1-XXh2';
% omega = sinc(omega./kernel_pars(1));
% end
elseif strcmp(kernel_type,'lin_kernel')
if nargin<4,
omega = Xtrain*Xtrain';
else
omega = Xtrain*Xt';
end
elseif strcmp(kernel_type,'poly_kernel')
if nargin<4,
omega = (Xtrain*Xtrain'+kernel_pars(1)).^kernel_pars(2);
else
omega = (Xtrain*Xt'+kernel_pars(1)).^kernel_pars(2);
end
% elseif strcmp(kernel_type,'wav_kernel')
% if nargin<4,
% XXh = sum(Xtrain.^2,2)*ones(1,nb_data);
% omega = XXh+XXh'-2*(Xtrain*Xtrain');
%
% XXh1 = sum(Xtrain,2)*ones(1,nb_data);
% omega1 = XXh1-XXh1';
% omega = cos(kernel_pars(3)*omega1./kernel_pars(2)).*exp(-omega./kernel_pars(1));
%
% else
% XXh1 = sum(Xtrain.^2,2)*ones(1,size(Xt,1));
% XXh2 = sum(Xt.^2,2)*ones(1,nb_data);
% omega = XXh1+XXh2' - 2*(Xtrain*Xt');
%
% XXh11 = sum(Xtrain,2)*ones(1,size(Xt,1));
% XXh22 = sum(Xt,2)*ones(1,nb_data);
% omega1 = XXh11-XXh22';
%
% omega = cos(kernel_pars(3)*omega1./kernel_pars(2)).*exp(-omega./kernel_pars(1));
% end
end
⛄ 运行结果
