【lssvm预测】基于EEMD结合改进鲸鱼算法优化最小支持向量机实现电力负荷预测（IWOA+LSSVM）含Matlab源码

1 简介

随着电力负荷的迅猛增长和用电环境的越发复杂,对短期负荷预测的精度与可靠性提出更高要求.为解决最小二乘支持向量机(LSSVM)算法中核参数和惩戒参数依赖经验选取导致的短期负荷预测精度较低,收敛速度较慢的问题,提出一种基于改进鲸鱼算法优化最小二乘支持向量机(IWOALSSVM)的负荷预测方法.首先通过引入非线性因子和自适应权重实现鲸鱼算法的改进,进而利用其改进算法对LSSVM参数进行寻优,最后建立短期负荷预测模型.结合某地区的实测数据进行预测分析,结果表明,相较于LSSVM,PSOLSSVM,改进模型预测精度和收敛速度均有大幅提高,在电力企业中具有良好的实际应用价值.

2 部分代码

%_________________________________________________________________________%% 鲸鱼优化算法             %%_________________________________________________________________________%% The Whale Optimization Algorithmfunction [Leader_score,Leader_pos,Convergence_curve]=WOA(SearchAgents_no,Max_iter,lb,ub,dim,fobj)​% initialize position vector and score for the leader Leader_pos=zeros(1,dim);Leader_score=inf; %change this to -inf for maximization problems​​%Initialize the positions of search agentsPositions=initialization(SearchAgents_no,dim,ub,lb);​Convergence_curve=zeros(1,Max_iter);​t=0;% Loop counter​% Main loopwhile t<Max_iter    for i=1:size(Positions,1)                % Return back the search agents that go beyond the boundaries of the search space        Flag4ub=Positions(i,:)>ub;        Flag4lb=Positions(i,:)<lb;        Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;                % Calculate objective function for each search agent        fitness=fobj(Positions(i,:));                % Update the leader        if fitness<Leader_score % Change this to > for maximization problem            Leader_score=fitness; % Update alpha            Leader_pos=Positions(i,:);        end            end        a=2-t*((2)/Max_iter); % a decreases linearly fron 2 to 0 in Eq. (2.3)        % a2 linearly dicreases from -1 to -2 to calculate t in Eq. (3.12)    a2=-1+t*((-1)/Max_iter);        % Update the Position of search agents     for i=1:size(Positions,1)        r1=rand(); % r1 is a random number in [0,1]        r2=rand(); % r2 is a random number in [0,1]                A=2*a*r1-a;  % Eq. (2.3) in the paper        C=2*r2;      % Eq. (2.4) in the paper                        b=1;               %  parameters in Eq. (2.5)        l=(a2-1)*rand+1;   %  parameters in Eq. (2.5)                p = rand();        % p in Eq. (2.6)                for j=1:size(Positions,2)                        if p<0.5                   if abs(A)>=1                    rand_leader_index = floor(SearchAgents_no*rand()+1);                    X_rand = Positions(rand_leader_index, :);                    D_X_rand=abs(C*X_rand(j)-Positions(i,j)); % Eq. (2.7)                    Positions(i,j)=X_rand(j)-A*D_X_rand;      % Eq. (2.8)                                    elseif abs(A)<1                    D_Leader=abs(C*Leader_pos(j)-Positions(i,j)); % Eq. (2.1)                    Positions(i,j)=Leader_pos(j)-A*D_Leader;      % Eq. (2.2)                end                            elseif p>=0.5                              distance2Leader=abs(Leader_pos(j)-Positions(i,j));                % Eq. (2.5)                Positions(i,j)=distance2Leader*exp(b.*l).*cos(l.*2*pi)+Leader_pos(j);                            end                    end    end    t=t+1;    Convergence_curve(t)=Leader_score;end​​​​

3 仿真结果

4 参考文献

[1]刘沛津, 胡冀飞, 贺宁,等. 改进鲸鱼算法优化LSSVM的短期电力负荷预测研究[J]. 现代电子技术, 2021, 44(13):5.

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