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Transformer-Architecture-for-Energy-Consumption-Prediction
Sari95/Transformer-Architecture-for-Energy-Consumption-Prediction
Transformer-Architecture-for-Energy-Consumption-Prediction is a machine learning model from Sari95. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as gpl.
This model applies Transformer architecture to predict energy consumption over a 48-hour period using historical energy usage and weather data from 2021 to 2023.
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Public
Updated May 22, 2024
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At a glance
- License
- gpl
- Access
- Public
- Created
- May 3, 2024
- Updated
- May 22, 2024
- SHA
- 3f5b4436
Model card
From the Hugging Face model README
Transformer-Architecture for Energy Consumption Prediction
Description
This model applies Transformer architecture to predict energy consumption over a 48-hour period using historical energy usage and weather data from 2021 to 2023.
Model Details
Model Type: Transformer
Data Period: 2021-2023
Variables Used:
- Transformer with Energy consumption data and weather data
- Transformer with Energy consumption data and two additional variables: 'Lastgang_Moving_Average' and 'Lastgang_First_Difference'
Features
The model uses a sequence length of 192 (48 hours) to create input sequences for training and testing. Positional encodings are added to the sequences to provide temporal information to the Transformer model.
Installation and Execution
To run this model, you need Python along with the following libraries:
pandasnumpymatplotlibscikit-learntorchgputilpsutiltorchsummary
Steps to Execute the Model:
-
Install Required Packages
-
Load your Data
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Preprocess the data according to the specifications
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Run the Script
- License
- gpl
- Created
- May 3, 2024
- Updated
- May 22, 2024