matlab 构建用于风电负荷预测 风光出力预测 风速预测的时序预测系统 DWT-BP（离散小波变换与反向传播神经网络）、DWT-SVR（离散小波变换与支持向量回归）以及LSTM（长短期记忆网络）

如何构建用于负荷预测、风光出力预测和风速预测的时序预测系统可以使用多种方法包括DWT-BP离散小波变换与反向传播神经网络、DWT-SVR离散小波变换与支持向量回归以及LSTM长短期记忆网络。代码示例仅供参考学习可用于负荷预测风光出力预测风速预测等时序预测 DWT-BP DWT-SVR LSTM文章目录如何构建用于负荷预测、风光出力预测和风速预测的时序预测系统可以使用多种方法包括DWT-BP离散小波变换与反向传播神经网络、DWT-SVR离散小波变换与支持向量回归以及LSTM长短期记忆网络。1. 数据准备2. DWT-BP 实现离散小波变换 (DWT)反向传播神经网络 (BPNN)3. DWT-SVR 实现支持向量回归 (SVR)4. LSTM 实现5. 结果评估1. 数据预处理 (data_process.m)2. DWT-BP 实现 (dwt_bp.m)3. DWT-SVR 实现 (dwt_svr.m)4. LSTM 实现 (lstm.m)5. 主程序 (main.m)11如何构建用于负荷预测、风光出力预测和风速预测的时序预测系统可以使用多种方法包括DWT-BP离散小波变换与反向传播神经网络、DWT-SVR离散小波变换与支持向量回归以及LSTM长短期记忆网络。代码示例仅供参考学习1. 数据准备假设同学已经有了历史数据集例如风速、负荷或风光出力的历史数据。以CSV文件的形式存储。importpandasaspdimportnumpyasnp# 加载数据datapd.read_csv(historical_data.csv)2. DWT-BP 实现离散小波变换 (DWT)importpywtdefdwt_decomposition(data,waveletdb4,level3):coeffspywt.wavedec(data,wavelet,levellevel)returncoeffsdefdwt_reconstruction(coeffs,waveletdb4):reconstructed_datapywt.waverec(coeffs,wavelet)returnreconstructed_data# 示例对数据进行DWT分解和重构coeffsdwt_decomposition(data[value].values)reconstructed_datadwt_reconstruction(coeffs)反向传播神经网络 (BPNN)fromsklearn.neural_networkimportMLPRegressorfromsklearn.model_selectionimporttrain_test_splitfromsklearn.preprocessingimportMinMaxScaler# 数据预处理scalerMinMaxScaler()scaled_datascaler.fit_transform(reconstructed_data.reshape(-1,1))X[]y[]foriinrange(len(scaled_data)-10):X.append(scaled_data[i:i10])y.append(scaled_data[i10])Xnp.array(X)ynp.array(y)X_train,X_test,y_train,y_testtrain_test_split(X,y,test_size0.2,random_state42)# 训练BPNN模型bpnn_modelMLPRegressor(hidden_layer_sizes(100,),max_iter500,activationrelu,solveradam)bpnn_model.fit(X_train,y_train.ravel())# 预测predictionsbpnn_model.predict(X_test)3. DWT-SVR 实现支持向量回归 (SVR)fromsklearn.svmimportSVR# 使用DWT分解后的数据训练SVR模型svr_modelSVR(kernelrbf,C1e3,gamma0.1)svr_model.fit(X_train,y_train.ravel())# 预测svr_predictionssvr_model.predict(X_test)4. LSTM 实现importtensorflowastffromtensorflow.keras.modelsimportSequentialfromtensorflow.keras.layersimportLSTM,Dense# 构建LSTM模型lstm_modelSequential()lstm_model.add(LSTM(50,activationrelu,input_shape(10,1)))lstm_model.add(Dense(1))lstm_model.compile(optimizeradam,lossmse)# 调整输入形状以适应LSTMX_train_lstmX_train.reshape((X_train.shape[0],X_train.shape[1],1))X_test_lstmX_test.reshape((X_test.shape[0],X_test.shape[1],1))# 训练LSTM模型lstm_model.fit(X_train_lstm,y_train,epochs50,batch_size64,validation_data(X_test_lstm,y_test),verbose1)# 预测lstm_predictionslstm_model.predict(X_test_lstm)5. 结果评估fromsklearn.metricsimportmean_squared_error,r2_score# 评估模型性能defevaluate_model(y_true,y_pred):msemean_squared_error(y_true,y_pred)r2r2_score(y_true,y_pred)print(fMSE:{mse}, R2 Score:{r2})evaluate_model(y_test,predictions)evaluate_model(y_test,svr_predictions)evaluate_model(y_test,lstm_predictions)如何使用DWT-BP、DWT-SVR和LSTM进行时序预测的基本流程。同学你可以根据具体需求调整参数和模型结构以获得更好的预测效果。示例包括数据预处理、DWT-BP、DWT-SVR以及LSTM的实现。以下是完整的代码实现1. 数据预处理 (data_process.m)function[X_train,X_test,y_train,y_test]data_process(data,waveletdb4,level3)% 数据归一化scalermatlab.net.url.ConnectionPool;scaled_datanormalize(data);% DWT分解coeffsdwt(scaled_data,wavelet,level);reconstructed_dataidwt(coeffs,wavelet,level);% 构建时间序列数据集nlength(reconstructed_data)-10;Xzeros(n,10);yzeros(n,1);fori1:nX(i,:)reconstructed_data(i:i9);y(i)reconstructed_data(i10);end% 划分训练集和测试集idxrandperm(length(y));trainIdxidx(1:round(0.8*length(y)));testIdxidx(round(0.8*length(y))1:end);X_trainX(trainIdx,:);X_testX(testIdx,:);y_trainy(trainIdx);y_testy(testIdx);end2. DWT-BP 实现 (dwt_bp.m)function[bpnn_model,predictions]dwt_bp(X_train,X_test,y_train,y_test)% 创建BP神经网络模型netfeedforwardnet([10,5]);net.trainFcntrainlm;% Levenberg-Marquardt算法net.divideFcn;% 不进行数据划分因为我们已经手动划分了% 训练BPNN模型nettrain(net,X_train,y_train);% 预测predictionsnet(X_test);end3. DWT-SVR 实现 (dwt_svr.m)function[svr_model,svr_predictions]dwt_svr(X_train,X_test,y_train,y_test)% 创建SVR模型svr_modelfitrsvm(X_train,y_train,KernelFunction,rbf,BoxConstraint,1);% 预测svr_predictionspredict(svr_model,X_test);end4. LSTM 实现 (lstm.m)function[lstm_model,lstm_predictions]lstm(X_train,X_test,y_train,y_test)% 调整输入形状以适应LSTMX_train_lstmreshape(X_train.,[10,size(X_train,1),1]);X_test_lstmreshape(X_test.,[10,size(X_test,1),1]);% 定义LSTM网络结构layers[sequenceInputLayer(1)lstmLayer(100,OutputMode,last)fullyConnectedLayer(1)regressionLayer];% 设置训练选项optionstrainingOptions(adam,...MaxEpochs,200,...MiniBatchSize,64,...InitialLearnRate,0.005,...GradientThreshold,1,...Verbose,0,...Plots,training-progress);% 训练LSTM模型lstm_modeltrainNetwork(X_train_lstm,y_train,layers,options);% 预测lstm_predictionspredict(lstm_model,X_test_lstm);end5. 主程序 (main.m)% 加载数据datacsvread(historical_data.csv);% 数据预处理[X_train,X_test,y_train,y_test]data_process(data);% DWT-BP预测[bpnn_model,bp_predictions]dwt_bp(X_train,X_test,y_train,y_test);% DWT-SVR预测[svr_model,svr_predictions]dwt_svr(X_train,X_test,y_train,y_test);% LSTM预测[lstm_model,lstm_predictions]lstm(X_train,X_test,y_train,y_test);% 结果评估mse_bpmean((y_test-bp_predictions).^2);mse_svrmean((y_test-svr_predictions).^2);mse_lstmmean((y_test-lstm_predictions).^2);fprintf(MSE (DWT-BP): %.4f\n,mse_bp);fprintf(MSE (DWT-SVR): %.4f\n,mse_svr);fprintf(MSE (LSTM): %.4f\n,mse_lstm);% 绘制结果figure;plot(y_test,b,LineWidth,2);hold on;plot(bp_predictions,r--,LineWidth,2);plot(svr_predictions,g-.,LineWidth,2);plot(lstm_predictions,m:,LineWidth,2);legend(真实值,DWT-BP预测,DWT-SVR预测,LSTM预测);xlabel(样本序号);ylabel(预测值);title(时序预测结果对比);如何使用MATLAB实现DWT-BP、DWT-SVR和LSTM进行时序预测的基本流程。