How to label an RBG image consisting of 8 bits/ data points and each point represents a certain power value to use the image as an input to CNN for forecasting

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Danya 2023-3-17

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https://ww2.mathworks.cn/matlabcentral/answers/1930690-how-to-label-an-rbg-image-consisting-of-8-bits-data-points-and-each-point-represents-a-certain-powe

编辑： Shreeya 2023-8-25

I have a set of power values recorded each hour, then created RGB images of those power values so each image represents 8 hours. The image attached represents 8 different power values (since each hour represents a single power value). I want to use those images as an input to CNN, but don't know how to because I can't label the image.

How can I label the images so I use them as an input to the CNN? Is there a code to do that on MATLAB?

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Pratham Shah 2023-4-5

Hi Danya,

I have few questions regarding your problem statement.

1- What kid of labels you want to give to your image?

2- As you said, you want to predict the future power value; which parameter are you going to give in the ML model for prediction?

3- If your model is going to predict the power value based on some numerical data, can't you use regression technique? Means is it necessary to use CNN?

Danya 2023-4-5

Hi Pratham,

1- I want to label it in such a way that each colour represents a power value. So when the labelled image is used as an input to the CNN, the CNN can read 8 different power value for each image.

2- I will predict the future power by using the labelled images as an input to CNN

3- It is possible to use regression (tried that before) but my task is to test if image-based prediction using CNN is more accurate than the regression thechnique, since CNN works well with images

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Shreeya 2023-8-25

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1930690-how-to-label-an-rbg-image-consisting-of-8-bits-data-points-and-each-point-represents-a-certain-powe#answer_1293452

编辑：Shreeya 2023-8-25

Hello Danya

To my understanding, you want to forecast power outputs represented as RGB values. Instead of labelling the dataset, you can use LSTMs since they are best suited for forecasting. A possible solution is:

Using a CNN Network to extract the essential image features and output a 1D vector, followed by an LSTM network.
Image Regeneration using Up Convolution method.
Segment the image vertically in 8 parts to predict the power values based on the colour gradient.

You can refer to the LSTM, CNN and up convolution documentations linked below respectively.

Long short-term memory (LSTM) layer for recurrent neural network (RNN) - MATLAB - MathWorks India

Train Convolutional Neural Network for Regression - MATLAB & Simulink - MathWorks India

Transposed 2-D convolution layer - MATLAB transposedConv2dLayer - MathWorks India

Hope this helps.