metadata

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:

Load your dataset into a pandas DataFrame.
Ensure that the data is formatted according to the specifications mentioned in the model details.
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.