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README.md

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+title: "Linear Regression Model for Energy Consumption Prediction"
+description: "This model predicts energy consumption based on meteorological data and historical usage."
 license: gpl
 ---
+
+# Linear Regression Model for Energy Consumption Prediction
+
+## Description
+This linear regression model predicts energy consumption based on meteorological data and historical energy usage from 2021 to 2023. It utilizes time series data from a transformer station to forecast future energy demands. It is built using the `statsmodels` library in Python and incorporates both time-based and weather-related variables to enhance prediction accuracy.
+
+## Model Details
+- **Model Type:** Linear Regression
+- **Data Period:** 2021-2023
+- **Variables Used:**
+  - `Lastgang`: Energy consumption data
+  - `Hour`: Hour of the day
+  - `DayOfWeek`: Day of the week
+  - `Lastgang_Moving_Average`: Moving average of energy consumption
+  - `Lastgang_First_Difference`: First difference of energy consumption
+
+## 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. It defines target variables (`Lastgang`) and explanatory variables including hourly and daily patterns as well as derived features from the consumption data. The dataset includes preprocessed features such as scaled energy consumption (`Lastgang`), and time-related features (`Hour`, `DayOfWeek`).
+
+## Installation and Execution
+To run this model, you need Python along with the following libraries:
+- `pandas`
+- `numpy`
+- `matplotlib`
+- `statsmodels`
+- `sklearn`
+
+To execute the model:
+1. Load your dataset into a pandas DataFrame.
+2. Ensure that the data is formatted according to the specifications mentioned in the model details.
+3. Run the script provided in the `Prediction_Linear-Regression.ipynb` notebook.
+
+## Contributions
+Contributions to this project are welcome. You can improve the existing model, add new features, or enhance the documentation. Please submit a pull request or open an issue if you have suggestions or need further information.