✅作者简介:热爱科研的Matlab仿真开发者,修心和技术同步精进,matlab项目合作可私信。
🍎个人主页:Matlab科研工作室
🍊个人信条:格物致知。
更多Matlab完整代码及仿真定制内容点击👇
🔥 内容介绍
随着全球对可再生能源的需求不断增长,风能作为一种清洁、可再生的能源形式,受到了广泛关注。风能的可预测性对于电力系统的稳定运行至关重要。因此,准确预测风电发电量对于电力行业的规划和运营至关重要。
在过去的几十年中,许多传统的预测方法已经被提出和应用于风电预测中,如回归分析、时间序列分析和人工神经网络等。然而,这些方法往往受到数据特性和模型复杂性的限制,导致预测精度不高。
近年来,基于多元宇宙算法的优化方法在解决复杂问题和优化模型方面取得了显著的成果。多元宇宙算法是一种模拟自然界中多元宇宙的进化过程的优化算法。它通过模拟宇宙的演化过程,不断更新和优化解空间中的解,并最终找到最优解。这种算法具有全局搜索能力和高效性,能够有效地应用于风电预测中。
核极限学习机(KELM)是一种基于核函数的机器学习算法,它在处理非线性问题上具有很好的性能。KELM通过随机生成隐藏层神经元的权重和阈值,将输入数据映射到高维特征空间,从而实现非线性映射。然后,通过线性回归来学习输出权重,从而得到最终的预测结果。然而,传统的KELM算法往往存在着参数选择困难和计算复杂度高的问题。
为了解决传统KELM算法的问题,我们将多元宇宙算法与KELM相结合,提出了MVO-KELM方法。MVO-KELM首先使用多元宇宙算法来优化KELM算法中的参数选择,包括隐藏层神经元的权重和阈值、核函数的参数等。然后,利用优化后的KELM模型对风电发电量进行回归预测。
通过实验验证,我们发现MVO-KELM在风电预测中具有较高的精度和稳定性。相比传统的预测方法,MVO-KELM能够更准确地预测风电发电量,提高电力系统的运行效率。此外,MVO-KELM还具有较低的计算复杂度,可以在实际应用中实现实时预测。
总之,基于多元宇宙算法优化核极限学习MVO-KELM实现风电回归预测是一种有效的方法。它能够提高风电预测的精度和稳定性,为电力行业的规划和运营提供有力支持。我们相信,随着技术的不断发展和研究的深入,MVO-KELM方法将在风电预测领域发挥越来越重要的作用。
📣 部分代码
%_______________________________________________________________________________________%% Multi-Verse Optimizer (MVO) source codes demo version 1.0 %% %% Developed in MATLAB R2011b(7.13) %% %% Author and programmer: Seyedali Mirjalili %% %% e-Mail: ali.mirjalili@gmail.com %% seyedali.mirjalili@griffithuni.edu.au %% %% Homepage: http://www.alimirjalili.com %% %% Main paper: %% %% S. Mirjalili, S. M. Mirjalili, A. Hatamlou %% Multi-Verse Optimizer: a nature-inspired algorithm for global optimization %% Neural Computing and Applications, in press,2015, %% DOI: http://dx.doi.org/10.1007/s00521-015-1870-7 %% %%_______________________________________________________________________________________%% You can simply define your cost in a seperate file and load its handle to fobj% The initial parameters that you need are:%__________________________________________% fobj = @YourCostFunction% dim = number of your variables% Max_iteration = maximum number of generations% SearchAgents_no = number of search agents% lb=[lb1,lb2,...,lbn] where lbn is the lower bound of variable n% ub=[ub1,ub2,...,ubn] where ubn is the upper bound of variable n% If all the variables have equal lower bound you can just% define lb and ub as two single number numbers% To run MVO: [Best_score,Best_pos,cg_curve]=MVO(Universes_no,Max_iteration,lb,ub,dim,fobj)%__________________________________________function [Best_universe_Inflation_rate,Best_universe,Convergence_curve]=MVO(N,Max_time,lb,ub,dim,fobj)%Two variables for saving the position and inflation rate (fitness) of the best universeBest_universe=zeros(1,dim);Best_universe_Inflation_rate=inf;%Initialize the positions of universesUniverses=initialization(N,dim,ub,lb);%Minimum and maximum of Wormhole Existence Probability (min and max in% Eq.(3.3) in the paperWEP_Max=1;WEP_Min=0.2;Convergence_curve=zeros(1,Max_time);%Iteration(time) counterTime=1;%Main loopwhile Time<Max_time+1 %Eq. (3.3) in the paper WEP=WEP_Min+Time*((WEP_Max-WEP_Min)/Max_time); %Travelling Distance Rate (Formula): Eq. (3.4) in the paper TDR=1-((Time)^(1/6)/(Max_time)^(1/6)); %Inflation rates (I) (fitness values) Inflation_rates=zeros(1,size(Universes,1)); for i=1:size(Universes,1) %Boundary checking (to bring back the universes inside search % space if they go beyoud the boundaries Flag4ub=Universes(i,:)>ub; Flag4lb=Universes(i,:)<lb; Universes(i,:)=(Universes(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb; %Calculate the inflation rate (fitness) of universes Inflation_rates(1,i)=fobj(Universes(i,:)); %Elitism if Inflation_rates(1,i)<Best_universe_Inflation_rate Best_universe_Inflation_rate=Inflation_rates(1,i); Best_universe=Universes(i,:); end end [sorted_Inflation_rates,sorted_indexes]=sort(Inflation_rates); for newindex=1:N Sorted_universes(newindex,:)=Universes(sorted_indexes(newindex),:); end %Normaized inflation rates (NI in Eq. (3.1) in the paper) normalized_sorted_Inflation_rates=normr(sorted_Inflation_rates); Universes(1,:)= Sorted_universes(1,:); %Update the Position of universes for i=2:size(Universes,1)%Starting from 2 since the firt one is the elite Back_hole_index=i; for j=1:size(Universes,2) r1=rand(); if r1<normalized_sorted_Inflation_rates(i) White_hole_index=RouletteWheelSelection(-sorted_Inflation_rates);% for maximization problem -sorted_Inflation_rates should be written as sorted_Inflation_rates if White_hole_index==-1 White_hole_index=1; end %Eq. (3.1) in the paper Universes(Back_hole_index,j)=Sorted_universes(White_hole_index,j); end if (size(lb,2)==1) %Eq. (3.2) in the paper if the boundaries are all the same r2=rand(); if r2<WEP r3=rand(); if r3<0.5 Universes(i,j)=Best_universe(1,j)+TDR*((ub-lb)*rand+lb); end if r3>0.5 Universes(i,j)=Best_universe(1,j)-TDR*((ub-lb)*rand+lb); end end end if (size(lb,2)~=1) %Eq. (3.2) in the paper if the upper and lower bounds are %different for each variables r2=rand(); if r2<WEP r3=rand(); if r3<0.5 Universes(i,j)=Best_universe(1,j)+TDR*((ub(j)-lb(j))*rand+lb(j)); end if r3>0.5 Universes(i,j)=Best_universe(1,j)-TDR*((ub(j)-lb(j))*rand+lb(j)); end end end end end %Update the convergence curve Convergence_curve(Time)=Best_universe_Inflation_rate; %Print the best universe details after every 50 iterations if mod(Time,50)==0 display(['At iteration ', num2str(Time), ' the best universes fitness is ', num2str(Best_universe_Inflation_rate)]); end Time=Time+1;end
⛳️ 运行结果