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⛄ 内容介绍
针对海上风电功率预测精度差的问题,提出一种改进的SSA-LSTM模型.选择在时间序列问题处理上具有良好性能的长短期记忆(LSTM)神经网络,通过寻优能力强、收敛速度快的麻雀搜索算法对LSTM网络隐含层神经元个数、学习率和训练次数等超参数进行优化,得到SSA-LSTM模型.采用江苏省盐城市某400 MW风电场功率数据进行算例分析,在不同条件变量下分别使用SSA-LSTM模型、LSTM模型预测,仿真结果表明,SSA-LSTM模型具有更高的预测精度、更好的预测稳定性.
⛄ 部分代码
function [fMin , bestX, Convergence_curve] = SSA( N, M, lb, ub, dim, fobj)
X=initialization(N,dim,ub,lb);
P_percent = 0.2; % 发现者的种群规模占总种群规模的百分比
pNum = round(N*P_percent); % 发现者数量20%
SD = pNum/2; % 警戒者数量10%
ST = 0.8; % 安全阈值
% 初始化
for i = 1:N
% X(i, :) = lb + (ub - lb) .* rand(1, dim);
fitness(i) = fobj(X(i, :));
end
pFit = fitness;
pX = X; % 与pFit相对应的个体最佳位置
[fMin, bestI] = min(fitness); % fMin表示全局最优解
bestX = X(bestI, :); % bestX表示全局最优位置
%% 迭代寻优
for t = 1 : M
[~, sortIndex] = sort(pFit); % 排序
[fmax, B] = max(pFit);
worst = X(B, :);
%% 发现者位置更新
r2 = rand(1);
if r2 < ST
for i = 1:pNum % Equation (3)
r1 = rand(1);
X(sortIndex(i), :) = pX(sortIndex(i), :)*exp(-(i)/(r1*M));
X(sortIndex(i), :) = Bounds(X(sortIndex(i), :), lb, ub);
fitness(sortIndex(i)) = fobj(X(sortIndex(i), :));
end
else
for i = 1:pNum
X(sortIndex(i), :) = pX(sortIndex(i), :)+randn(1)*ones(1, dim);
X(sortIndex(i), :) = Bounds(X(sortIndex(i), :), lb, ub);
fitness(sortIndex(i)) = fobj(X(sortIndex(i), :));
end
end
[~, bestII] = min(fitness);
bestXX = X(bestII, :);
%% 跟随者位置更新
for i = (pNum+1):N % Equation (4)
A = floor(rand(1, dim)*2)*2-1;
if i > N/2
X(sortIndex(i), :) = randn(1)*exp((worst-pX(sortIndex(i), :))/(i)^2);
else
X(sortIndex(i), :) = bestXX+(abs((pX(sortIndex(i), :)-bestXX)))*(A'*(A*A')^(-1))*ones(1, dim);
end
X(sortIndex(i), :) = Bounds(X(sortIndex(i), :), lb, ub);
fitness(sortIndex(i)) = fobj(X(sortIndex(i), :));
end
%% 警戒者位置更新
c = randperm(numel(sortIndex));
b = sortIndex(c(1:SD));
for j = 1:length(b) % Equation (5)
if pFit(sortIndex(b(j))) > fMin
X(sortIndex(b(j)), :) = bestX+(randn(1, dim)).*(abs((pX(sortIndex(b(j)), :) -bestX)));
else
X(sortIndex(b(j)), :) = pX(sortIndex(b(j)), :)+(2*rand(1)-1)*(abs(pX(sortIndex(b(j)), :)-worst))/(pFit(sortIndex(b(j)))-fmax+1e-50);
end
X(sortIndex(b(j)), :) = Bounds(X(sortIndex(b(j)), :), lb, ub);
fitness(sortIndex(b(j))) = fobj(X(sortIndex(b(j)), :));
end
for i = 1:N
% 更新个体最优
if fitness(i) < pFit(i)
pFit(i) = fitness(i);
pX(i, :) = X(i, :);
end
% 更新全局最优
if pFit(i) < fMin
fMin = pFit(i);
bestX = pX(i, :);
end
end
Convergence_curve(t) = fMin;
disp(['SSA: At iteration ', num2str(t), ' ,the best fitness is ', num2str(fMin)]);
end
%% 边界处理
function s = Bounds(s, Lb, Ub)
% 下界
temp = s;
I = temp < Lb;
temp(I) = Lb(I);
% 上界
J = temp > Ub;
temp(J) = Ub(J);
% 更新
s = temp;
⛄ 运行结果
⛄ 参考文献
[1]李森文, 张伟, 李纯宇,等. 基于SSA-LSTM的海上风电功率预测[J]. 机械与电子, 2022(040-006).
