【TCN回归预测】基于双向时间卷积神经网络BiTCN实现光伏回归预测附matlab代码.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 res =xlsread('data.xlsx','sheet1'); %% 数据分析 num_size = 0.7; % 训练集占数据集比例 outdim = 1; % 最后一列为输出 num_samples = size(res, 1); % 样本个数 res = res(randperm(num_samples), :); % 打乱数据集（不希望打乱时，注释该行） num_train_s = round(num_size * num_samples); % 训练集样本个数 f_ = size(res, 2) - outdim; % 输入特征维度 %% 划分训练集和测试集 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); %% 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 = 3; % 卷积核大小 dropoutFactor = 0.05; % 空间丢失因子 numBlocks = 1; % 残差块个数 numFeatures = 7; % 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 layers = [ fullyConnectedLayer(outdim, Name = "fc") regressionLayer]; lgraph = addLayers(lgraph, layers); % 将输出层加入到网络 lgraph = connectLayers(lgraph, outputName, "fc"); % 连接输出层到网络最后 %% 查看网络结构 figure plot(lgraph) title("Temporal Convolutional Network") %% 设置训练参数 options = trainingOptions("adam", ... % 优化器Adam InitialLearnRate = 1e-2, ... % 初始学习率为0.01 MaxEpochs = 1000, ... % 最大训练次数 miniBatchSize = 1, ... % 批大小 Plots = "training-progress", ... % 显示训练曲线 Verbose = 0); % 关闭命令行 %% 训练网络 net = trainNetwork(pc_train, tc_train, lgraph, options); %% 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 = mapminmax('reverse', T_sim1, ps_output); T_sim2 = mapminmax('reverse', T_sim2, ps_output); %% 均方根误差 error1 = sqrt(sum((T_sim1 - T_train).^2) ./ M); error2 = sqrt(sum((T_sim2 - T_test ).^2) ./ N); figure plot(T_sim1,'b-.o','linewidth',2) hold on plot(T_train,'g-s','linewidth',2) hold on legend('真实值','TCN预测值') xlabel('测试样本编号') ylabel('指标值') title('训练集TCN模型预测值和真实值对比图') figure plot(T_sim2,'b-.o','linewidth',2) hold on plot(T_test,'g-s','linewidth',2) hold on legend('真实值','TCN预测值') xlabel('测试样本编号') ylabel('指标值') title('测试集TCN模型预测值和真实值对比图') figure plot(error1,'b-s','markerfacecolor','g') hold on plot(error2,'r-p','markerfacecolor','g') legend('训练集预测误差','测试集预测误差') xlabel('测试样本编号') ylabel('预测偏差') %% 绘图 figure plot(T_sim1,'-s','Color',[255 0 0]./255,'linewidth',1,'Markersize',5,'MarkerFaceColor',[250 0 0]./255) hold on plot(T_train,'-o','Color',[150 150 150]./255,'linewidth',0.8,'Markersize',4,'MarkerFaceColor',[150 150 150]./255) legend( 'TCN拟合训练数据','实际分析数据','Location','NorthWest','FontName','华文宋体'); title('TCN模型预测结果及真实值','fontsize',12,'FontName','华文宋体') xlabel('样本','fontsize',12,'FontName','华文宋体'); ylabel('数值','fontsize',12,'FontName','华文宋体'); xlim([1 M]) %------------------------------------------------------------------------------------- figure bar((T_sim1 - T_train)./T_train) legend('TCN模型训练集相对误差','Location','NorthEast','FontName','华文宋体') title('TCN模型训练集相对误差','fontsize',12,'FontName','华文宋体') ylabel('误差','fontsize',12,'FontName','华文宋体') xlabel('样本','fontsize',12,'FontName','华文宋体') xlim([1 M]); %------------------------------------------------------------------------------------- figure plot(T_sim2,'-s','Color',[0 0 255]./255,'linewidth',1,'Markersize',5,'MarkerFaceColor',[0 0 255]./255) hold on plot(T_test,'-o','Color',[0 0 0]./255,'linewidth',0.8,'Markersize',4,'MarkerFaceColor',[0 0 0]./255) legend('TCN预测测试数据','实际分析数据','Location','NorthWest','FontName','华文宋体'); title('TCN模型预测结果及真实值','fontsize',12,'FontName','华文宋体') xlabel('样本','fontsize',12,'FontName','华文宋体'); ylabel('数值','fontsize',12,'FontName','华文宋体'); xlim([1 N]) %------------------------------------------------------------------------------------- figure bar((T_sim2 - T_test )./T_test) legend('TCN模型测试集相对误差','Location','NorthEast','FontName','华文宋体') title('TCN模型测试集相对误差','fontsize',12,'FontName','华文宋体') ylabel('误差','f