BiTCN-BiGRU-Attention实现负荷多变量时间序列预测附matlab代码 标准.rar

clc; clear close all addpath(genpath(pwd)) X = xlsread('风电场预测.xlsx'); X = X(5665:8640,:); %选取3月份数据 num_samples = length(X); % 样本个数 kim = 10; % 延时步长（kim个历史数据作为自变量） zim = 1; % 跨zim个时间点进行预测 or_dim = size(X,2); % 重构数据集 for i = 1: num_samples - kim - zim + 1 res(i, :) = [reshape(X(i: i + kim - 1,:), 1, kim*or_dim), X(i + kim + zim - 1,:)]; end % 训练集和测试集划分 outdim = 1; % 最后一列为输出 num_size = 0.9; % 训练集占数据集比例 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 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); end for i = 1 : N vp_test{i, 1} = p_test(:, i); vt_test{i, 1} = t_test(:, i); end %% 优化算法优化前，构建优化前的TCN_GRU_Attention模型 outputSize = 1; %数据输出y的维度 numFilters = 64; filterSize = 5; dropoutFactor = 0.1; numBlocks = 2; layer = sequenceInputLayer(f_,Normalization="rescale-symmetric",Name="input"); lgraph = layerGraph(layer); outputName = layer.Name; % 建立网络结构 -- 残差块 for i = 1 : numBlocks % 膨胀因子 dilationFactor = 2^(i-1); % 创建TCN正向支路 layers = [ convolution1dLayer(filterSize, numFilters, DilationFactor = dilationFactor, Padding = "causal", Name="conv1_" + i) % 一维卷积层 layerNormalizationLayer % 层归一化 dropoutLayer(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 dropoutLayer(dropoutFactor) % 空间丢弃层 additionLayer(4, Name = "add_" + i) ]; % 添加残差块到网络 lgraph = addLayers(lgraph, layers); % 连接卷积层到残差块 lgraph = connectLayers(lgraph, outputName, "conv1_" + i); % 创建 TCN反向支路flip网络结构 Fliplayers = [ FlipLayer("flip_" + i) % 反向翻转 convolution1dLayer(1, numFilters, Name = "convSkip_"+i); % 反向残差连接 convolution1dLayer(filterSize, numFilters, DilationFactor = dilationFactor, Padding = "causal", Name="conv2_" + i) % 一维卷积层 layerNormalizationLayer % 层归一化 dropoutLayer(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 dropoutLayer(dropoutFactor, Name="drop" + i) % 空间丢弃层 ]; % 添加 flip 网络结构到网络 lgraph = addLayers(lgraph, Fliplayers); % 连接 flip 卷积层到残差块 lgraph = connectLayers(lgraph, outputName, "flip_" + i); lgraph = connectLayers(lgraph, "drop" + i, "add_" + i + "/in3"); lgraph = connectLayers(lgraph, "convSkip_"+i, "add_" + i + "/in4"); % 残差连接 -- 首层 if i == 1 % 建立残差卷积层 % Include convolution in first skip connection. layer = convolution1dLayer(1,numFilters,Name="convSkip"); lgraph = addLayers(lgraph,layer); lgraph = connectLayers(lgraph,outputName,"convSkip"); lgraph = connectLayers(lgraph,"convSkip","add_" + i + "/in2"); else lgraph = connectLayers(lgraph,outputName,"add_" + i + "/in2"); end % Update layer output name. outputName = "add_" + i; end tempLayers = flattenLayer("Name","flatten"); lgraph = addLayers(lgraph,tempLayers); tempLayers = gruLayer(10,"Name","gru1"); lgraph = addLayers(lgraph,tempLayers); tempLayers = [ FlipLayer("flip3") gruLayer(10,"Name","gru2")]; lgraph = addLayers(lgraph,tempLayers); tempLayers = [ concatenationLayer(1,2,"Name","concat") selfAttentionLayer(1,50,"Name","selfattention") %单头注意力Attention机制，把1改为2,3,4……即为多头，后面的50是键值 fullyConnectedLayer(outdim,"Name","fc") regressionLayer("Name","regressionoutput")]; lgraph = addLayers(lgraph,tempLayers); 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', 60, ... % 最大训练次数 'GradientThreshold', 1, ... % 梯度阈值 'InitialLearnRate', 0.01, ... % 初始学习率 'L2Regularization', 0.0001, ... % 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 % 网络训练 tic net0 = trainNetwork(vp_train,vt_train,lgraph,options0); toc %% 测试与评估 t_sim = net0.predict(vp_test); analyzeNetwork(net0);% 查看网络结构 % 数据反归一化 T_sim = mapminmax('reverse', t_sim, ps_output); % 数据格式转换 T_sim = cell2mat(T_sim); T_sim = T_sim'; %% GJO优化TCN-GRU-Attention disp(' ') disp('优化BiTCN_GRU_attention神经网络：') popsize=2; %初始种群规模 maxgen=10; %最大进化代数 fobj = @(x)objectiveFunction(x,f_,vp_train,vt_train,vp_test,T_test,ps_output); % RIME优化参数设置 Best_pos = [0.001 10 2 0.0001]; %参数的下限。分别是学习率，GRU的神经元个数，注意力机制的键值, 正则化参数 %% 比较算法预测值 str={'真实值','BiTCN-BiGRU-Attention'}; figure('Units', 'pixels', ... 'Position', [300 300 860 370]); plot(T_test,'-','Color',[0.8500 0.3250 0.0980]) hold on plot(T_sim,'-.','Color',[0.4940 0.1840 0.5560]) hold on legend(str) set (gca,"FontSize",12,'LineWidth',1.2) box off legend Box off %% 比较�