|
--- |
|
title: TBATS Model for Energy Consumption Prediction |
|
description: >- |
|
This model predicts energy consumption based on meteorological data and |
|
historical usage. |
|
license: gpl |
|
--- |
|
|
|
# TBATS Model for Energy Consumption Prediction |
|
|
|
## Description |
|
This TBATS model predicts 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:** TBATS (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) |
|
**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: |
|
- `pandas` |
|
- `tbats` |
|
- `numpy` |
|
- `matplotlib` |
|
- `scikit-learn` |
|
|
|
### Steps to Execute the Model: |
|
1. **Install Required Packages** |
|
|
|
2. **Load your Data** |
|
|
|
3. **Preprocess the data according to the specifications** |
|
|
|
4. **Run the Script** |
|
|