I am modeling Hybrid model for load forecasting. I have ran the HW and FOA part but when I merge LSTM then I am getting error of "TrainNetwork"

Question

Muhammad Farhan 2023-12-28

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2064676-i-am-modeling-hybrid-model-for-load-forecasting-i-have-ran-the-hw-and-foa-part-but-when-i-merge-lst

评论： Ben 2024-1-9

I have four years of Electricity data from a city for load forecasting and I am unable to train my LSTM network and getting the error again and again.

My LSTM modeling is as follow:

% Prepare Data for LSTM

% Calculate Residuals

residuals = actual_data_2021 - forecasted_values_2021;

% Ensure residuals is a column vector

residuals = residuals(:);

% Prepare Data for LSTM

sequence_length = 24; % Number of time steps

num_samples = length(residuals) - sequence_length;

X = zeros(num_samples, sequence_length); % Preallocate for efficiency

Y = zeros(num_samples, 1); % Output for LSTM

for i = 1:num_samples

X(i, :) = residuals(i:i+sequence_length-1); % Last 24 hours data

Y(i) = residuals(i + sequence_length); % Next hour data

end

% Alternative Approach: Using `permute` for Reshaping

X = permute(X, [1, 2, 3]);

% Check dimensions of X and Y

disp(['Size of X: ', mat2str(size(X))]);

disp(['Size of Y: ', mat2str(size(Y))]);

% Define LSTM Network Architecture

numFeatures = 1; % Number of features (univariate time series)

numHiddenUnits = 50; % Number of hidden units in LSTM layer

numResponses = 1; % Number of responses (single value prediction)

layers = [ ...

sequenceInputLayer(numFeatures)

lstmLayer(numHiddenUnits)

fullyConnectedLayer(numResponses)

regressionLayer];

% Training Options

options = trainingOptions('adam', ...

'MaxEpochs',100, ...

'GradientThreshold',1, ...

'InitialLearnRate',0.005, ...

'LearnRateSchedule','piecewise', ...

'LearnRateDropPeriod',50, ...

'LearnRateDropFactor',0.2, ...

'Verbose',0, ...

'Plots','training-progress');

% Train LSTM

net = trainNetwork(X, Y, layers, options);

and I am getting the following error again and again.

LSTM_model

Size of X: [8688 24]

Size of Y: [8688 1]

Error using trainNetwork

The training sequences are of feature dimension 8688

but the input layer expects sequences of feature

dimension 1.

Error in LSTM_model (line 69)

net = trainNetwork(X, Y, layers, options);

Can anyone suggest me how to resolve this problem please?

I would highly appreacite and if needed I can pay for it.

Email: Farhanee.suit@gmail.com

Thanks

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Ben 2024-1-5

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2064676-i-am-modeling-hybrid-model-for-load-forecasting-i-have-ran-the-hw-and-foa-part-but-when-i-merge-lst#answer_1383906

When you have multiple time-series observations you need to put the data into cell arrays. This is because each time-series can potentially be a different sequence length in general. Here's an example with the sizes you have:

% Set up fake data with same sizes

num_samples = 8688;

sequence_length = 24;

X = zeros(num_samples,sequence_length);

Y = zeros(num_samples,1);

% Convert to cell array

Xc = num2cell(X,2);

Note that now Xc is a cell array, and there are num_samples entries in Xc, and Xc{i} has size 1 x sequence_length.

Note that the way your data seems to be set up is a sequence-to-one task, so the target data Y aren't sequences. In this case you need a model that outputs non-sequence data, e.g. by specifying OutputMode="last" in lstmLayer. Here's some dummy code to show training can work:

net = [sequenceInputLayer(1);lstmLayer(1,OutputMode="last");regressionLayer];

opts = trainingOptions("adam",MaxEpochs=1);

trainednet = trainNetwork(Xc,Y,net,opts);

2 个评论
显示无隐藏无

Muhammad Farhan 2024-1-9

thank you so much for your valuable answer.

It works but I am getting another error now.

Here is an error window.

Can you also help me to resolve this problem please?

Million of thanks in Advance.

Thanks

""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""

>> LSTM_model

Training on single CPU.

|========================================================================================|

|========================================================================================|

| 1 | 1 | 00:00:00 | 0.05 | 1.3e-03 | 0.0010 |

| 1 | 50 | 00:00:00 | 0.03 | 3.6e-04 | 0.0010 |

| 1 | 67 | 00:00:00 | 0.06 | 1.8e-03 | 0.0010 |

|========================================================================================|

Training finished: Max epochs completed.

Warning: Data sample time is assumed to be 1 seconds. To specify a different value for the data

sample time consider providing data using a timetable or an iddata object.

> In idpack.iodata.extractRefDataFromAnalysisInputList>localDeduceSampling (line 412)

In idpack.iodata.extractRefDataFromAnalysisInputList (line 108)

In idmodel.parseCompareResidPredictInputs (line 66)

In predict (line 83)

In LSTM_model (line 69)

Error using predict

The number of inputs and outputs of the model must match that of the data.

Error in LSTM_model (line 69)

lstmForecastedResiduals = predict(net, X);

""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""

Ben 2024-1-9

Unfortunately I'm not familiar with the functions here where the error is occuring such as idpack.iodata.extractRefDataFromAnalysisInputList>localDeduceSampling

Do you have custom layers that call these functions?

请先登录，再进行评论。

Answer 2

Venu 2024-1-7

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2064676-i-am-modeling-hybrid-model-for-load-forecasting-i-have-ran-the-hw-and-foa-part-but-when-i-merge-lst#answer_1384656

编辑：Venu 2024-1-7

Hi @Muhammad Farhan,

The 'permute' function is used to rearrange the dimensions of an array without changing the total number of dimensions.

In your code:

X = permute(X, [1, 2, 3]);

If X were initially a 2D array, 'permute' would not add a third dimension; it would only rearrange existing dimensions if you specified a different order. You need to reshape it to a 3D array with the second dimension being 1 (since you have a univariate time series).

X = reshape(X, [sequence_length, 1, num_samples]);

Hope this helps!