SARIMAX-for-Energy-Consumption-Prediction

SARIMAX Model for Energy Consumption Prediction

Description

This SARIMAX model predicts energy consumption over a 48-hour period based on meteorological data and historical energy usage from 2021 to 2023. It utilizes time series data from a transformer station and incorporates both time-based and weather-related variables to enhance prediction accuracy.

Model Details

Model Type: SARIMAX (Seasonal ARIMA with exogenous variables)
Data Period: 2021-2023
Variables Used:

• Lastgang: Energy consumption data
• StundenwertStrahlung: Hourly radiation
• Globalstrahlung_15Min: Global radiation every 15 minutes
• StrahlungGeneigteFläche: Radiation on inclined surfaces
• TheorPVProd: Theoretical photovoltaic production
• Direktnormalstrahlung: Direct normal radiation
• Schönwetterstrahlung: Clear sky radiation
• Lufttemperatur: Air temperature
• 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 weather-related features.

Installation and Execution

To run this model, you need Python along with the following libraries:

• pandas
• numpy
• matplotlib
• statsmodels
• scikit-learn
• pmdarima
• psutil

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