TCN-LSTM-Multihead-Attention TCN-LSTM LSTM TCN实现风电预测Matlab.rar

%% 清空环境变量 warning off % 关闭报警信息 close all % 关闭开启的图窗 clear % 清空变量 clc % 清空命令行 %% 导入数据 data= xlsread('data.xlsx'); %% 划分训练集和测试集 [MM,NN]=size(data); SS=randperm(MM); train_num=round(MM*0.8); ceshi_num=MM-round(MM*0.8); P_train = data((1:train_num), 1: NN-1)'; T_train = data((1:train_num), NN)'; M = size(P_train, 2); P_test = data((train_num+1:MM), 1: NN-1)'; T_test =data((train_num+1:MM), NN)'; 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); %% TCN_LSTM_Attention %% 数据格式转换 pc_train{1, 1} = P_train; pc_test {1, 1} = P_test ; tc_train{1, 1} = t_train; tc_test {1, 1} = t_test ; %% 设置网络参数 numFilters = 16; % 卷积核个数 filterSize = 5; % 卷积核大小 dropoutFactor = 0.2; % 空间丢失因子 numBlocks = 1; % 残差块个数 numFeatures = NN-1; % 特征个数 %% 输入层结构 layer = sequenceInputLayer(numFeatures, Normalization = "rescale-symmetric", Name = "input"); %% 将输入层加入空白网络 lgraph = layerGraph(layer); outputName = layer.Name; %% 建立网络结构 -- 残差块 for i = 1 : numBlocks dilationFactor = 2 ^(i - 1); % 膨胀因子 % 建立网络结构 layers = [ convolution1dLayer(filterSize, numFilters, DilationFactor = dilationFactor, ... Padding = "causal", Name="conv1_" + i) % 一维卷积层 layerNormalizationLayer % 层归一化 spatialDropoutLayer(dropoutFactor) % 空间丢弃层 convolution1dLayer(filterSize, numFilters, ... DilationFactor = dilationFactor, Padding = "causal") % 一维卷积层 layerNormalizationLayer % 层归一化 reluLayer % 激活层 spatialDropoutLayer(dropoutFactor) % 空间丢弃层 additionLayer(2, Name = "add_" + i)]; % 合并层 lgraph = addLayers(lgraph, layers); % 将卷积层加入到网络 lgraph = connectLayers(lgraph, outputName, "conv1_" + i); % 将模块的卷积层首层和残差结构连接 % 残差连接 -- 首层 if i == 1 layer = convolution1dLayer(1, numFilters, Name = "convSkip"); % 建立残差卷积层 lgraph = addLayers(lgraph, layer); % 将残差卷积层加入到网络 lgraph = connectLayers(lgraph, outputName, "convSkip"); % 将残差卷积层 lgraph = connectLayers(lgraph, "convSkip", "add_" + i + "/in2"); % 将跳跃残差层连接到 addtion 层 输入口2 else lgraph = connectLayers(lgraph, outputName, "add_" + i + "/in2"); % 将残差层连接到 addtion 层 输入口2 end % 更新输出层名字 outputName = "add_" + i; end %% 网络输出层 % 添加 LSTM 层 % lstmLayer = lstmLayer(hiddenSize, 'Name', 'lstm'); lstmLayer = lstmLayer(100, 'Name', 'lstm'); lgraph = addLayers(lgraph, lstmLayer); lgraph = connectLayers(lgraph, outputName, 'lstm'); % 添加全连接层 layers = [ selfAttentionLayer(numFeatures,numFeatures,"Name","multihead-attention") % 多头注意力机制+多通道 fullyConnectedLayer(1, 'Name', 'fc') regressionLayer]; lgraph = addLayers(lgraph, layers); lgraph = connectLayers(lgraph, 'lstm', 'multihead-attention'); %设置权重衰减参数实现 L2 正则化 weightDecay = 0.01; % 正则化项权重 % 更新每个卷积层、LSTM 层和全连接层的权重衰减参数 numLayers = numel(lgraph.Layers); %获取网络中层的数量 for i = 1:numLayers layer = lgraph.Layers(i); if isa(layer, 'CausalConvolution1DLayer') || isa(layer, 'nnet.cnn.layer.FullyConnectedLayer') %检查当前层是否为因果卷积层、LSTM 层或全连接层。如果是，则执行以下操作 layer.WeightLearnRateFactor = 1; layer.WeightL2Factor = weightDecay; end end %% 设置训练参数 options = trainingOptions('adam', ... % Adam 梯度下降算法 'MaxEpochs', 1000, ... % 最大迭代次数 'MiniBatchSize',16,... % 批处理 'InitialLearnRate', 1e-3, ... % 初始学习率为0.001 'LearnRateSchedule', 'piecewise', ... % 学习率下降 'LearnRateDropFactor', 0.1, ... % 学习率下降因子 0.1 'LearnRateDropPeriod', 500, ... % 经过训练后 学习率为 0.001 * 0.1 'Shuffle', 'every-epoch', ... % 每次训练打乱数据集 'Plots', 'training-progress', ... % 画出曲线 'Verbose', 1); % 打印 %% 训练网络 [net,traininfo]= trainNetwork(pc_train, tc_train, lgraph, options); %% 查看网络结构 analyzeNetwork(net) %% 仿真预测 t_sim1 = predict(net, pc_train); t_sim2 = predict(net, pc_test ); %% 格式转换 T_sim1 = double(t_sim1{1, 1}); T_sim2 = double(t_sim2{1, 1}); %% 数据反归一化 T_sim1_TCN_LSTM_Attention = mapminmax('reverse', T_sim1, ps_output); T_sim2_TCN_LSTM_Attention = mapminmax('reverse', T_sim2, ps_output); % [T_sim1_TCN_LSTM_Attention,T_sim2_TCN_LSTM_Attention]=TCN_LSTM_Attention(p_train, t_train,p_test,t_test , ps_output,NN);%测试集 [R1_TCN_LSTM_Attention,rmse1_TCN_LSTM_Attention,biaozhuncha1_TCN_LSTM_Attention,mae1_TCN_LSTM_Attention,mape1_TCN_LSTM_Attention]=calc_error(T_train,T_sim1_TCN_LSTM_Attention);%训练集 [R2_TCN_LSTM_Attention,rmse2_TCN_LSTM_Attention,biaozhuncha2_TCN_LSTM_Attention,mae2_TCN_LSTM_Attention,mape2_TCN_LSTM_Attention]=calc_error(T_test,T_sim2_TCN_LSTM_Attention);%测试集 %% TCN_LSTM % [T_sim1_TCN_LSTM,T_sim2_TCN_LSTM]=TCN_LSTM(p_train,p_test,t_train,t_test, ps_output,NN); %% 数据格式转换 tc_test {1, 1} = t_test ; %% 设置网络参数 numFilters = 16; % 卷积核个数 filterSize = 5; % 卷积核大小 dropoutFactor = 0.2; % 空间丢失因子 numBlocks = 1; % 残差块个数 numFeatures = NN-1; % 特征个数 %% 输入层结构 layer = sequenceInputLayer(numFeatures, Normalization = "rescale-symmetric", Name = "input"); %% 将输入层加入空白网络 lgraph = layerGraph(layer); outputName = layer.Name; %% 建立网络结构 -- 残差块 for i = 1 : numBlocks dilationFactor = 2 ^(i - 1); % 膨胀因子 % 建立网络结构 layers = [ convolution1dLayer(filterSize, numFilters, DilationFactor = dilationFactor, ... Padding = "causal", Name="conv1_" + i) % 一维卷积层 layerNormalizationLayer % 层归一化 spatialDropoutLayer(dropoutFactor) % 空间丢弃层 convolution1dLayer(filterSize, numFilters, ... DilationFactor = dilationFactor, Padding = "causal") % 一维卷积层