Energy Demand Forecasting using ARIMA

This project aims to forecast energy demand using the Autoregressive Integrated Moving Average (ARIMA) model. Accurate energy demand forecasting is essential for optimizing power generation and distribution, reducing operational costs, and preventing energy shortages during peak periods.

Project Overview

The model.ipynb notebook provides a comprehensive analysis and implementation of the ARIMA model for energy demand forecasting. The process involves data loading, preprocessing, visualization, model training, and evaluation.

Table of Contents

• Introduction
• Dataset
• Methodology
  • Data Preprocessing
  • ARIMA Modeling
• Results
• Requirements
• Usage
• References

Introduction

Accurate forecasting of electricity demand is crucial for utility companies to optimize power generation and distribution. This project utilizes the ARIMA model, a popular time series forecasting technique, to predict future energy demand based on historical data.

Dataset

The dataset used in this project includes historical electricity demand data, which is essential for training and validating the forecasting model. Ensure that the dataset is placed in the appropriate directory before running the notebook.

Methodology

Data Preprocessing

• Loading Data: The dataset is loaded into a Pandas DataFrame for analysis.
• Datetime Conversion: Timestamps are converted to datetime objects to facilitate time series analysis.
• Visualization: Time series plots are generated to visualize trends, seasonality, and anomalies in the data.

ARIMA Modeling

• Stationarity Check: Statistical tests (e.g., Augmented Dickey-Fuller test) are performed to check for stationarity.
• Differencing: If the series is non-stationary, differencing is applied to achieve stationarity.
• Parameter Selection: The parameters (p, d, q) for the ARIMA model are selected using techniques like the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots.
• Model Training: The ARIMA model is trained on the preprocessed data.
• Forecasting: The trained model is used to forecast future energy demand.

Results

The forecasting performance of the ARIMA model is evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results indicate the model's accuracy in predicting future energy demand.

Requirements

To run the notebook, ensure you have the following Python libraries installed:

• pandas
• numpy
• matplotlib
• statsmodels

You can install these dependencies using pip:

pip install pandas numpy matplotlib statsmodels

Usage

1. Clone the Repository:

  git clone https://github.com/sameer-at-git/Energy-Demand-Forecasting-using-ARIMA.git

2. Navigate to the Directory:

cd Energy-Demand-Forecasting-using-ARIMA

3. Install Dependencies:

pip install -r requirements.txt

4. Run the Notebook:

Open and execute the model.ipynb notebook using Jupyter Notebook or Jupyter Lab:

jupyter notebook model.ipynb

References

• Time Series Analysis and Forecasting with ARIMA
• A Complete Tutorial on Time Series Modeling in Python