Downloads · 30 days
0
LSTM-for-Energy-Consumption-Prediction
xc962514/LSTM-for-Energy-Consumption-Prediction
LSTM-for-Energy-Consumption-Prediction is a machine learning model from xc962514. 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 Long Short-Term Memory (LSTM) architecture to predict energy consumption over a 48-hour period using historical energy usage and weather data from 2021 to 2023.
Access
Public
Updated Aug 12, 2026
Repo size
—
Likes
0
Public
Repo makeup
Click a slice to open those files.
.ipynb221 KB · 99%
At a glance
- License
- gpl
- Access
- Public
- Created
- Aug 12, 2026
- Updated
- Aug 12, 2026
- SHA
- b7950140
Model card
From the Hugging Face model README
LSTM for Energy Consumption Prediction
Description
This model applies Long Short-Term Memory (LSTM) 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: LSTM
Data Period: 2021-2023
Variables Used:
- LSTM with Energy consumption data and weather data
- LSTM 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.
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
-
Preprocess the Data According to the Specifications
-
Run the Script
- License
- gpl
- Created
- Aug 12, 2026
- Updated
- Aug 12, 2026