|
--- |
|
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. |
|
|
