效果一览

基本介绍

1.负荷预测 | Matlab基于TCN-LSTM-Attention单变量时间序列多步预测；
2.单变量时间序列数据集，采用前12个时刻预测未来96个时刻的数据；
3.excel数据方便替换，运行环境matlab2023及以上，展示最后96个时间步的预测对比图，评价指标MAE、MAPE、RMSE、MSE、R2；
4.程序语言为matlab，程序可出预测效果图，迭代优化图，相关分析图;
5.代码特点：参数化编程、参数可方便更改、代码编程思路清晰、注释明细。

Attention（注意力机制）：注意力机制用于加强模型对序列中不同位置的重要性的关注。通过计算每个时间步的注意力权重，模型可以自动学习并关注序列中最相关的部分。

程序设计

• 完整程序和数据获取方式：私信博主回复Matlab基于TCN-LSTM-Attention单变量时间序列多步预测。

P_train = res(1: num_train_s, 1: f_)';
T_train = res(1: num_train_s, f_ + 1: end)';
M = size(P_train, 2);P_test = res(num_train_s + 1: end, 1: f_)';
T_test = res(num_train_s + 1: end, f_ + 1: end)';
N = size(P_test, 2);%  数据归一化
[p_train, ps_input] = mapminmax(P_train, 0, 1);
p_test = mapminmax('apply', P_test, ps_input);[t_train, ps_output] = mapminmax(T_train, 0, 1);
t_test = mapminmax('apply', T_test, ps_output);%  格式转换
for i = 1 : M vp_train{i, 1} = p_train(:, i);vt_train{i, 1} = t_train(:, i);
endfor i = 1 : N vp_test{i, 1} = p_test(:, i);vt_test{i, 1} = t_test(:, i);
end
lgraph = connectLayers(lgraph,outputName,"flatten");
lgraph = connectLayers(lgraph,"flatten","gru1");
lgraph = connectLayers(lgraph,"flatten","flip3");
lgraph = connectLayers(lgraph,"gru1","concat/in1");
lgraph = connectLayers(lgraph,"gru2","concat/in2");%  参数设置
options0 = trainingOptions('adam', ...                 % 优化算法Adam'MaxEpochs', 150, ...                            % 最大训练次数'GradientThreshold', 1, ...                       % 梯度阈值'InitialLearnRate', 0.01, ...         % 初始学习率'LearnRateSchedule', 'piecewise', ...             % 学习率调整'LearnRateDropPeriod',100, ...                   % 训练100次后开始调整学习率'LearnRateDropFactor',0.001, ...                    % 学习率调整因子'L2Regularization', 0.001, ...         % 正则化参数'ExecutionEnvironment', 'cpu',...                 % 训练环境'Verbose', 1, ...                                 % 关闭优化过程'Plots', 'none');                    % 画出曲线

参考资料

[1] http://t.csdn.cn/pCWSp
[2] https://download.csdn.net/download/kjm13182345320/87568090?spm=1001.2014.3001.5501
[3] https://blog.csdn.net/kjm13182345320/article/details/129433463?spm=1001.2014.3001.5501