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⛄ 内容介绍
风电功率预测能为电网规划和运行提供重要依据,传统预测方法多为点预测,其结果一般有不同程度的误差,区间预测方法能有效描述风电输出功率的不确定性因而逐步受到重视。针对短期风电功率概率区间预测问题,提出一种基于麻雀优化的核极限学习机(SSA-KELM)模型,用于风电功率区间预测。通过核极限学习机(KELM)建立预测模型,采用麻雀算法对KELM的输出权值进行优化,寻找最优预测区间上下限,充分利用了KELM学习速度快、泛化能力强的优点,实现了对风电功率的快速区间预测。通过与KELM模型对比分析风电场在不同置信水平下的概率预测结果,发现SSA-KELM模型的预测精度更高,速度更快,能够为风电功率区间预测及风电并网安全稳定运行提供决策支持。
⛄ 部分代码
function [Convergence_curve,bestX]=SSA(N, dim, ub, lb,M,hiddennum_best, inputn, outputn, output_train, inputn_test ,outputps, output_test)
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
X = initialization(N, dim, ub, lb);
for i = 1:N
% X(i, :) = lb + (ub - lb) .* rand(1, dim);
fitness1(i) = fitness(X(i, :),hiddennum_best, inputn, outputn, output_train, inputn_test ,outputps, output_test);
end
pFit = fitness1;
pX = X; % 与pFit相对应的个体最佳位置
[fMin, bestI] = min(fitness1); % fMin表示全局最优解
bestX = X(bestI, :); % bestX表示全局最优位置
%% 开始进化
Convergence_curve = ones(M,1); % 初始化每次迭代得到的最佳的适应度
%% 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);
fitness1(sortIndex(i)) = fitness(X(sortIndex(i), :),hiddennum_best, inputn, outputn, output_train, inputn_test ,outputps, output_test);
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);
fitness1(sortIndex(i)) = fitness(X(sortIndex(i), :),hiddennum_best, inputn, outputn, output_train, inputn_test ,outputps, output_test);
end
end
[~, bestII] = min(fitness1);
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);
fitness1(sortIndex(i)) = fitness(X(sortIndex(i), :),hiddennum_best, inputn, outputn, output_train, inputn_test ,outputps, output_test);
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);
fitness1(sortIndex(b(j))) = fitness(X(sortIndex(b(j)), :),hiddennum_best, inputn, outputn, output_train, inputn_test ,outputps, output_test);
end
for i = 1:N
% 更新个体最优
if fitness1(i) < pFit(i)
pFit(i) = fitness1(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
⛄ 运行结果
⛄ 参考文献
[1]李军, 李大超. 基于优化核极限学习机的风电功率时间序列预测[J]. 物理学报, 2016(13):10.
[2]呼梦颖, 杨霈轶, 段建东, et al. 基于麻雀搜索算法优化核极限学习机的风电功率预测方法:, CN114065610A[P]. 2022.
