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Exponential-Smoothing-for-Energy-Consumption-Prediction
Sari95/Exponential-Smoothing-for-Energy-Consumption-Prediction
Exponential-Smoothing-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 Triple, Single, and Double Exponential Smoothing techniques to predict energy consumption over a 48-hour period based on historical energy usage from 2021 to 2023. It utilizes time series data from…
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Updated May 22, 2024
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At a glance
- License
- gpl
- Access
- Public
- Created
- May 3, 2024
- Updated
- May 22, 2024
- SHA
- dff10c70
Model card
From the Hugging Face model README
Exponential Smoothing Model for Energy Consumption Prediction
Description
This model applies Triple, Single, and Double Exponential Smoothing techniques to predict energy consumption over a 48-hour period based on historical energy usage from 2021 to 2023. It utilizes time series data from a transformer station to forecast future energy demands.
Model Details
Model Type: Exponential Smoothing (Triple, Single, and Double)
Data Period: 2021-2023
Variables Used:
Lastgang: Energy consumption data
Features
The model splits the data into training and testing sets, with the last 192 data points (equivalent to 48 hours at 15-minute intervals) designated as the test dataset. The dataset includes preprocessed features such as interpolated and aggregated energy consumption data (Lastgang).
Installation and Execution
To run this model, you need Python along with the following libraries:
pandasnumpymatplotlibscikit-learnstatsmodels
Steps to Execute the Model:
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Install Required Packages
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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