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README.md

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-license: gpl
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+---
+title: TBATS Model for Energy Consumption Prediction
+description: >-
+  This model predicts energy consumption based on meteorological data and
+  historical usage.
+license: gpl
+---
+
+# 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:
+- `pandas`
+- `numpy`
+- `matplotlib`
+- `scikit-learn`
+- `statsmodels`
+
+### 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**