1 简介
为了解决短期负荷预测精度低,准确性差等问题,采用一种使用灰狼优化算法(GWO)优化长短期记忆网络(LSTM)的短期负荷预测模型.针对模型的参数较难选择的问题,利用GWO对LSTM模型参数寻优.通过实例验证了预测模型的有效性,结果表明GWO-LSTM比LSTM模型具有更好的效果.
1.1 灰狼优化算法
1.2 LSTM模型
1.3 基于鲸鱼算法优化LSTM流程
2 部分代码
%% Get cuckoos by ramdom walk
function nest=get_cuckoos(nest,best,Lb,Ub)
% Levy flights
n=size(nest,1);
% Levy exponent and coefficient
% For details, see equation (2.21), Page 16 (chapter 2) of the book
% X. S. Yang, Nature-Inspired Metaheuristic Algorithms, 2nd Edition, Luniver Press, (2010).
beta=3/2;
sigma=(gamma(1+beta)sin(pibeta/2)/(gamma((1+beta)/2)beta2^((beta-1)/2)))^(1/beta);
for j=1:n,
s=nest(j,:);
% This is a simple way of implementing Levy flights
% For standard random walks, use step=1;
%% Levy flights by Mantegna's algorithm
u=randn(size(s))*sigma;
v=randn(size(s));
step=u./abs(v).^(1/beta);
% In the next equation, the difference factor (s-best) means that
% when the solution is the best solution, it remains unchanged.
stepsize=0.001step. (s-best);
% Here the factor 0.01 comes from the fact that L/100 should the typical
% step size of walks/flights where L is the typical lenghtscale;
% otherwise, Levy flights may become too aggresive/efficient,
% which makes new solutions (even) jump out side of the design domain
% (and thus wasting evaluations).
% Now the actual random walks or flights
s=s+stepsize.*randn(size(s));
% Apply simple bounds/limits
nest(j,:)=simplebounds(s,Lb,Ub);
end
% Application of simple constraints
function s=simplebounds(s,lb,ub)
Flag4ub=s>ub;
Flag4lb=s<lb;
s=s. (~(Flag4ub+Flag4lb))+ub. Flag4ub+lb.*Flag4lb;
% Apply the lower bound
% ns_tmp=s;
% I=ns_tmp<Lb;
% ns_tmp(I)=Lb(I);
%
% % Apply the upper bounds
% J=ns_tmp>Ub;
% ns_tmp(J)=Ub(J);
% % Update this new move
% s=ns_tmp;
3 仿真结果
4 参考文献
[1]曹开田, 高莘尧, 姜梦彦. 一种基于鲸鱼算法优化LSTM的频谱感知方法:.
[2]刘昊东, 邹必昌. 基于鲸鱼算法优化长短期记忆网络的短期负荷预测[J]. 电子世界(3):2.
