市政协启动"推进高职院校资源整合优化"课题调研

clc; clear close all X = xlsread('data.xlsx'); X = X(5665:8640,:); %选取3月份数据 load vmd_data.mat IMF = u; disp('…………………………………………………………………………………………………………………………') disp('VMD-CNNBiLSTM预测') disp('…………………………………………………………………………………………………………………………') %% 对每个分量建模 for uu=1:size(IMF,2) X_imf=[X(:,1:end-1),IMF(:,uu)]; num_samples = length(X_imf); % 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_imf,2); % 重构数据集 for i = 1: num_samples - kim - zim + 1 res(i, :) = [reshape(X_imf(i: i + kim - 1,:), 1, kim*or_dim), X_imf(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 = 0.7; % 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); %% 数据平铺 for i = 1:size(P_train,2) trainD{i,:} = (reshape(p_train(:,i),size(p_train,1),1,1)); end for i = 1:size(p_test,2) testD{i,:} = (reshape(p_test(:,i),size(p_test,1),1,1)); end targetD = t_train; targetD_test = t_test; numFeatures = size(p_train,1); layers0 = [ ... % 输入特征 sequenceInputLayer([numFeatures,1,1],'name','input') %输入层设置 sequenceFoldingLayer('name','fold') %使用序列折叠层对图像序列的时间步长进行独立的卷积运算。 % CNN特征提取 convolution2dLayer([3,1],16,'Stride',[1,1],'name','conv1') %添加卷积层，64，1表示过滤器大小，10过滤器个数，Stride是垂直和水平过滤的步长 batchNormalizationLayer('name','batchnorm1') % BN层，用于加速训练过程，防止梯度消失或梯度爆炸 reluLayer('name','relu1') % ReLU激活层，用于保持输出的非线性性及修正梯度的问题 % 池化层 maxPooling2dLayer([2,1],'Stride',2,'Padding','same','name','maxpool') % 第一层池化层，包括3x3大小的池化窗口，步长为1，same填充方式 % 展开层 sequenceUnfoldingLayer('name','unfold') %独立的卷积运行结束后，要将序列恢复 %平滑层 flattenLayer('name','flatten') bilstmLayer(25,'Outputmode','last','name','hidden1') dropoutLayer(0.2,'name','dropout_1') % Dropout层，以概率为0.2丢弃输入 fullyConnectedLayer(1,'name','fullconnect') % 全连接层设置（影响输出维度）（cell层出来的输出层） % regressionLayer('Name','output') ]; lgraph0 = layerGraph(layers0); lgraph0 = connectLayers(lgraph0,'fold/miniBatchSize','unfold/miniBatchSize'); %% Set the hyper parameters for unet training 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',70, ... % 训练75次后开始调整学习率 'LearnRateDropFactor',0.01, ... % 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 % % start training % 训练 net = trainNetwork(trainD,targetD',lgraph0,options0); %analyzeNetwork(net);% 查看网络结构 % 预测 t_sim1 = predict(net, trainD); t_sim2 = predict(net, testD); % 数据反归一化 T_sim1 = mapminmax('reverse', t_sim1, ps_output); T_sim2 = mapminmax('reverse', t_sim2, ps_output); T_train1 = T_train; T_test2 = T_test; % 数据格式转换 imf_T_sim1(:,uu) = double(T_sim1);% cell2mat将cell元胞数组转换为普通数组 imf_T_sim2(:,uu) = double(T_sim2); end 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 T_train = res(1: num_train_s, f_ + 1: end)'; T_test = res(num_train_s + 1: end, f_ + 1: end)'; %% 各分量预测的结果相加 T_sim_a = sum(imf_T_sim1,2); T_sim_b = sum(imf_T_sim2,2); VMDCNNBiLSTM_TSIM1 = T_sim_a'; VMDCNNBiLSTM_TSIM2 = T_sim_b'; save VMDCNNBiLSTM VMDCNNBiLSTM_TSIM1 VMDCNNBiLSTM_TSIM2 % 指标计算 disp('…………训练集误差指标…………') [mae1,rmse1,mape1,error1]=calc_error(T_train,T_sim_a'); fprintf('\n') figure plot(T_train); hold on plot(T_sim_a') legend('真实值','预测值') title('VMD-CNN-BiLSTM训练集预测效果对比') xlabel('样本点') ylabel('发电功率') disp('…………测试集误差指标…………') [mae2,rmse2,mape2,error2]=calc_error(T_test,T_sim_b'); fprintf('\n') figure plot(T_test); hold on plot(T_sim_b') legend('真实值','预测值') title('VMD-CNN-BiLSTM预测集预测效果对比') xlabel('样本点') ylabel('发电功率') figure plot(T_sim_b'-T_test) title('VMD-CNN-BiLSTM-误差曲线图') xlabel('样本点') ylabel('发电功率')