---
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.