Energy Consumption Forecasting

Key Metrics

0.038

kWh MAE

99.98%

R² Score

1M+

Records Analyzed

95%

MAE Improvement

Project Overview

Developed a machine learning regression model on 1M+ household power consumption records from 2006-2010 achieving 0.038 kWh Mean Absolute Error for real-time energy forecasting. The system analyzes historical consumption patterns including global active power, reactive power, voltage and sub-metering data to predict future energy usage.

Performed extensive feature engineering and exploratory data analysis using Python with pandas and scikit-learn to identify peak consumption patterns crucial for grid optimization. The Linear Regression model achieved an exceptional 99.98% R² score with 95% improvement in MAE, demonstrating highly accurate predictions for household power consumption enabling optimized grid management and cost reduction.

Core Algorithm

1 Load 1M+ household power consumption records from CSV dataset

2 Clean data by replacing missing values and removing 4,069 null entries

3 Extract temporal features (hour, day, month) from datetime columns

4 Apply StandardScaler normalization to numerical features

5 Train Linear Regression model on 80% training data split

6 Evaluate model performance using MAE, RMSE, and R² metrics

7 Generate predictions and visualize residuals for validation

Technologies Used

Python Pandas NumPy scikit-learn Matplotlib Seaborn Linear Regression

Technical Implementation

Data Preprocessing: Handled 1M+ records with 0.39% missing values using dropna() and feature normalization
Feature Engineering: Extracted temporal features (hour, day, month) capturing daily and seasonal consumption patterns
Model Training: Linear Regression with StandardScaler achieving 99.98% R² on 80-20 train-test split
Performance Analysis: Generated residual plots, actual vs predicted visualizations, and error distribution analysis

Key Features

Time Series Analysis: Identified peak consumption hours at 8-9 AM and 8-9 PM for demand management
Comprehensive EDA: 10+ visualizations revealing consumption patterns and seasonal variations
High Accuracy: 99.98% R² score demonstrating excellent model fit and prediction reliability
Scalable Pipeline: Efficient data processing supporting real-time forecasting applications

Challenges & Solutions

Large Dataset Processing: Handled 1M+ records efficiently using pandas optimization and chunked processing techniques
Missing Values: Implemented cleaning pipeline handling 4,069 missing entries (0.39% of data) through strategic removal
Feature Selection: Applied Recursive Feature Elimination identifying most significant predictors while maintaining performance

Results & Impact

The model achieved exceptional performance with 0.038 kWh Mean Absolute Error and 99.98% R² score, demonstrating highly accurate predictions for household energy consumption. The 95% improvement in MAE compared to baseline models indicates significant advancement in forecasting accuracy.

Identified critical insights including peak consumption periods (morning 8-9 AM and evening 8-9 PM) and seasonal patterns essential for grid optimization. The analysis revealed strong correlations between global active power, voltage and current consumption, enabling utility providers to make data-driven decisions for efficient energy distribution and load management.

AI-Optimized Energy Consumption Forecasting System