QRCNN-BiGRU-Attention卷积神经网络-双向门控循环单元分位数回归区间预测附Matlab完整源码

%% 此程序为单变量输入单步预测 %% 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 %% 导入数据 data = readmatrix('风电场预测.xlsx'); data = data(5665:6665,15); %选取部分数据，第15列为风电功率 [h1,l1]=data_process(data,8); %单步预测%步长为8，采用前8个时刻的风电功率预测第9个时刻的风电功率 res = [h1,l1]; num_samples = size(res,1); %样本个数 % 训练集和测试集划分 outdim = 1; % 最后一列为输出 num_size = 0.8; % 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 save_net = []; for i = 0.02 : 0.05 : 0.97 % 置信区间范围 0.97 - 0.02 = 0.95 %% 网络搭建 numFeatures = size(p_train,1); %% 网络搭建CNN-BiGRU-Attention lgraph = layerGraph(); % 添加层分支 % 将网络分支添加到层次图中。每个分支均为一个线性层组。 tempLayers = sequenceInputLayer([numFeatures,1,1],"Name","sequence"); lgraph = addLayers(lgraph,tempLayers); tempLayers = [ convolution2dLayer([2 1],8,"Name","conv","Padding","same") batchNormalizationLayer("Name","batchnorm") reluLayer("Name","relu") maxPooling2dLayer([2 1],"Name","maxpool","Padding","same") flattenLayer("Name","flatten_1") fullyConnectedLayer(10,"Name","fc_1")]; lgraph = addLayers(lgraph,tempLayers); 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(1,3,"Name","concat") fullyConnectedLayer(outdim,"Name","fc") selfAttentionLayer(1,5,"Name","selfattention") %Attention机制 QRegressionLayer('out', i)]; lgraph = addLayers(lgraph,tempLayers); % 清理辅助变量 clear tempLayers; % 连接层分支 % 连接网络的所有分支以创建网络图。 lgraph = connectLayers(lgraph,"sequence","conv"); lgraph = connectLayers(lgraph,"sequence","flatten"); lgraph = connectLayers(lgraph,"flatten","gru1"); lgraph = connectLayers(lgraph,"flatten","flip3"); lgraph = connectLayers(lgraph,"gru1","concat/in1"); lgraph = connectLayers(lgraph,"gru2","concat/in2"); lgraph = connectLayers(lgraph,"fc_1","concat/in3"); % 参数设置 options = trainingOptions('adam', ... % 优化算法Adam 'MaxEpochs', 150, ... % 最大训练次数 'GradientThreshold', 1, ... % 梯度阈值 'InitialLearnRate', 0.001, ... % 初始学习率 'Shuffle', 'every-epoch', ... % 训练打乱数据集 'ExecutionEnvironment', 'cpu',... % 训练环境 'Verbose', 1, ... % 关闭优化过程 'Plots', 'none'); % 画出曲线 % 训练 net = trainNetwork(vp_train, vt_train, lgraph, options); % 保存网络 save_net = [save_net, net]; end %% 采用不同网络进行预测 for i = 1 : length(save_net) i % 仿真预测 t_sim1(i, :) = predict(save_net(i), vp_train); t_sim2(i, :) = predict(save_net(i), vp_test ); % 数据反归一化 L_sim1{i} = cell2mat(mapminmax('reverse', t_sim1(i, :), ps_output)); L_sim2{i} = cell2mat(mapminmax('reverse', t_sim2(i, :), ps_output)); tt_sim1(i, :) = cell2mat(mapminmax('reverse', t_sim1(i, :), ps_output)); tt_sim2(i, :) = cell2mat(mapminmax('reverse', t_sim2(i, :), ps_output)); end %% 得到预测均值 T_sim1 = mean(tt_sim1); T_sim2 = mean(tt_sim2); %% 性能评估 error1 = sqrt(sum((T_sim1 - T_train) .^2 ) ./ M); error2 = sqrt(sum((T_sim2 - T_test ) .^2 ) ./ N); %% 绘图 figure fill([1 : M, M : -1 : 1], [L_sim1{1}, L_sim1{end}(end : -1 : 1)], ... 'r', 'FaceColor', [1, 0.8, 0.8], 'EdgeColor', 'none') hold on plot(1 : M, T_train, 'r-', 1 : M, T_sim1', 'b-', 'LineWidth', 0.3) legend('95%的置信区间', '真实值', '预测值') xlabel('预测样本') ylabel('预测结果') string = {'QRCNN-BiGRU-Attention训练集预测结果对比'; ['RMSE = ' num2str(error1)]}; title(string) xlim([1, M]) grid figure fill([1 : N, N : -1 : 1], [L_sim2{1}, L_sim2{end}(end : -1 : 1)], ... 'r', 'FaceColor', [1, 0.8, 0.8], 'EdgeColor', 'none') hold on plot(1 : N, T_test, 'r-', 1 : N, T_sim2', 'b-', 'LineWidth', 1) legend('95%的置信区间', '真实值', '预测值') xlabel('预测样本') ylabel('预测结果') string = {'QRCNN-BiGRU-Attention测试集预测结果对比'; ['RMSE = ' num2str(error2)]}; title(string) xlim([1, N]) grid %% 相关指标计算 % 指标计算 disp('…………QRCNN-BiGRU-Attention训练集误差指标…………') [mae1,rmse1,mape1,error1]=calc_error(T_train,T_sim1); fprintf('\n') disp('…………QRCNN-BiGRU-Attention测试集误差指标…………') [mae1,rmse1,mape1,error1]=calc_error(T_test,T_sim2); fprintf('\n') %% 指标计算(区间覆盖率和区间平均宽度百分比) picp1 = PICP (tt_sim1, T_train'); pinaw1 = PINAW(tt_sim1, T_train'); disp(['训练集的区间覆盖率为:', num2str(picp1), '。区间平均宽度百分比为:', num2str(pinaw1)]) picp2 = PICP (tt_sim2, T_test'); pinaw2 = PINAW(tt_sim2, T_test'); disp(['测试集的区间覆盖率为:', num2str(picp2), '。区间平均宽度百分比为:', num2str(pinaw2)])