基于双向长短期神经网络的居民用电功率预测，基于gru神经网络的居民用电功率预测（代码完整，数据齐全）

%% GRULSTM network % Programmed by 红松科技工作室,2027771338 % Updated 6 Apr. 2024. clc clear close all %读取double格式数据 [num1,ax,ay] = xlsread('daily_power_consumption.csv'); num =[]; for ii = 1:length(num1)-5 num = [num;[reshape(num1(ii:ii+3,:),1,12) num1(ii,2:3) num1(ii,1)]]; end n = randperm(length(num)); n = 1:length(num); m=4000; input_train =num(n(1:m),1:14 );%训练数据输出数据 output_train = num(n(1:m),15);%训练数据输入数据 input_test = num(n(m+1:end),1:14 );%测试数据输出数据 output_test = num(n(m+1:end),15);%测试数据输入数据 [inputn,inputps]=mapminmax(input_train',-1,1);%训练数据的输入数据的归一化 [outputn,outputps]=mapminmax(output_train',-1,1);%训练数据的输出数据的归一化de inputn_test=mapminmax('apply',input_test',inputps); %% Define Network Architecture % Define the network architecture. numFeatures = 14;%输入层维度 numResponses = 1;%输出维度 % 200 hidden units numHiddenUnits = 50;%第一层维度 % a fully connected layer of size 50 & a dropout layer with dropout probability 0.5 fungru % Specify the training options. % Train for 60 epochs with mini-batches of size 20 using the solver 'adam' maxEpochs = 60;%最大迭代次数 miniBatchSize =2;%最小批量 % the learning rate == 0.01 % set the gradient threshold to 1 % set 'Shuffle' to 'never' options = trainingOptions('adam', ... %解算器 'MaxEpochs',maxEpochs, ... %最大迭代次数 'MiniBatchSize',miniBatchSize, ... %最小批次 'InitialLearnRate',0.01, ... %初始学习率 'GradientThreshold',0.1, ... %梯度阈值 'Shuffle','every-epoch', ... %打乱顺序 'Plots','training-progress',... %画图 'Verbose',0); %不输出训练过程 %% Train the Network net = trainNetwork(inputn,outputn,layers,options);%开始训练 % save maydata.mat %% Test the Network y_pred = predict(net,inputn_test,'MiniBatchSize',miniBatchSize)';%测试仿真输出 [y_pred]=(mapminmax('reverse',y_pred',outputps)); % y_pred0 = predict(net,inputn,'MiniBatchSize',1)';%训练拟合值 % y_pred0=(mapminmax('reverse',y_pred0',outputps))'; y_pred=(double(y_pred))'; figure%打开一个图像窗口 plot(y_pred(:,1),'k-o')%黑色实线，点的形状为* hold on%继续画图 plot(output_test(:,1),'r--*')%红色实线，点的形状为o hold off%停止画图 title('测试图')%标题 ylabel('power')%Y轴名称 legend('测试输出incidence1','实际incidence1')%标签 error1 = y_pred-output_test;%误差 figure plot(error1(:,1),'k-*') title('测试误差图') [MSE,RMSE,MBE,MAE ] =MSE_RMSE_MBE_MAE(output_test(:,1),y_pred(:,1)); result_table = table; result_table.simd = y_pred(:,1); result_table.trued = output_test(:,1); writetable(result_table,'./结果.csv')