Predictive Analysis of Energy Consumption Patterns using Machine Learning

Project Overview

The objective of this project is to develop a machine learning model that predicts household energy consumption over hourly, daily, and weekly intervals. By leveraging historical energy usage data, the model captures consumption patterns to support efficient energy management strategies.

Methodology

Data Collection

Historical data is collected from individual household smart meters.

Data Preprocessing

• Handling missing values using mean imputation
• Resampling data to hourly, daily, and weekly intervals
• Normalization using Min-Max scaling for uniform feature distribution

Model Training

A machine learning regression model (XGBoost) is trained on the preprocessed data to predict energy consumption patterns.

Evaluation

Model performance is evaluated using the following metrics:

• Root Mean Squared Error (RMSE)
• Mean Squared Error (MSE)
• Mean Absolute Error (MAE)

Dataset

Source: Individual Household Electric Power Consumption Dataset (UCI Repository)

Technologies & Frameworks Used

• Programming Language: Python 🐍
• Libraries: scikit-learn, NumPy, Pandas, Matplotlib
• Environment: Google Colab / Jupyter Notebook