Sari95/Exponential-Smoothing-for-Energy-Consumption-Prediction

Exponential Smoothing Model for Energy Consumption Prediction

Description

This model applies Triple, Single, and Double Exponential Smoothing techniques to predict energy consumption over a 48-hour period based on historical energy usage from 2021 to 2023. It utilizes time series data from a transformer station to forecast future energy demands.

Model Details

Model Type: Exponential Smoothing (Triple, Single, and Double)
Data Period: 2021-2023
Variables Used:

• Lastgang: Energy consumption data

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. The dataset includes preprocessed features such as interpolated and aggregated energy consumption data (Lastgang).

Installation and Execution

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

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

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