lstm风电预测matlab
时间: 2023-10-14 20:05:44 浏览: 51
使用LSTM进行风电预测的MATLAB代码如下:
```matlab
% 加载数据
data = csvread('wind_power_data.csv');
power = data(:,2);
% 数据归一化
minPower = min(power);
maxPower = max(power);
powerNorm = (power - minPower) / (maxPower - minPower);
% 设置LSTM网络参数
nFeatures = 1;
nHiddenUnits = 100;
nOutputs = 1;
nEpochs = 100;
miniBatchSize = 10;
learningRate = 0.01;
% 创建LSTM网络
layers = [ ...
sequenceInputLayer(nFeatures)
lstmLayer(nHiddenUnits)
fullyConnectedLayer(nOutputs)
regressionLayer];
% 指定训练选项
options = trainingOptions('adam', ...
'MaxEpochs',nEpochs, ...
'MiniBatchSize',miniBatchSize, ...
'LearnRateSchedule','piecewise', ...
'LearnRateDropFactor',0.1, ...
'LearnRateDropPeriod',50, ...
'GradientThreshold',1, ...
'SequenceLength','longest', ...
'Shuffle','never', ...
'Verbose',0, ...
'Plots','training-progress');
% 训练LSTM网络
net = trainNetwork(powerNorm,layers,options);
% 预测未来的风电功率
futurePowerNorm = predict(net,powerNorm(end-99:end));
futurePower = futurePowerNorm * (maxPower - minPower) + minPower;
% 显示预测结果
plot(1:length(power),power,'b',length(power):length(power)+length(futurePower)-1,futurePower,'r');
legend('实际功率','预测功率');
```
需要注意的是,此代码只是一个简单的示例,实际应用中需要根据数据的特点进行修改和优化。另外,为了使LSTM网络更加准确,可能需要对数据进行更加细致的预处理和特征工程。