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--- |
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title: TBATS Model for Energy Consumption Prediction |
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description: >- |
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This model predicts energy consumption based on meteorological data and |
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historical usage. |
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license: gpl |
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--- |
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# Exponential Smoothing Model for Energy Consumption Prediction |
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## Description |
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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. |
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## Model Details |
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**Model Type:** Exponential Smoothing (Triple, Single, and Double) |
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**Data Period:** 2021-2023 |
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**Variables Used:** |
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- `Lastgang`: Energy consumption data |
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## Features |
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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`). |
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## Installation and Execution |
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To run this model, you need Python along with the following libraries: |
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- `pandas` |
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- `numpy` |
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- `matplotlib` |
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- `scikit-learn` |
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- `statsmodels` |
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### Steps to Execute the Model: |
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1. **Install Required Packages** |
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2. **Load your Data** |
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3. **Preprocess the data according to the specifications** |
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4. **Run the Script** |
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