Energy Consumption Insights

Skills, Tech Stack, and Libraries

1. Skills: Data Wrangling, Predictive Analytics, Statistical Modeling, Data Visualization

2. Tech Stack: Python, SQL, Power BI, Tableau

3. Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn

Approach

Objective:

I developed an analytics solution to identify household energy consumption patterns and recommend strategies for energy optimization. The project aimed to reduce energy costs and promote sustainable consumption habits.

Approach:

1. Data Collection and Preprocessing:

   • Gathered energy usage data from smart meters, covering daily and hourly consumption readings.

   • Cleaned and preprocessed the data using Pandas, ensuring consistency by addressing missing values and removing anomalies caused by faulty meter readings.

2. Exploratory Data Analysis (EDA):

   • Conducted EDA using Matplotlib and Seaborn to identify trends in energy usage, peak consumption times, and seasonal patterns.

   • Grouped data by household, appliance type, and time intervals to derive granular insights.

3. Feature Engineering:

   • Created new features such as average daily consumption, peak-hour usage, and appliance-level energy contributions.

   • Categorized households into consumption tiers (low, medium, high) based on their usage.

4. Predictive Modeling:

   • Developed a regression model using Scikit-learn to predict future energy usage based on historical patterns and external factors such as weather conditions.

   • Trained a classification model to identify households likely to exceed energy usage thresholds, triggering alerts.

Dashboard Design:

Built an interactive dashboard in Tableau to present:

1. Real-time energy consumption by household and appliance type.

2. Peak usage hours and seasonal trends.

3. Forecasted energy usage with actionable recommendations.

Automation:

Automated data ingestion and model updates using Python scripts, ensuring the dashboard remained current and actionable.

Code Flow:

1. Load energy usage data into Pandas for cleaning and preprocessing.

2. Perform EDA to uncover patterns and trends.

3. Create features for predictive and classification models.

4. Train and evaluate models using Scikit-learn.

5. Visualize results in Tableau and deploy the dashboard for stakeholders.

Results

The project delivered an advanced energy analytics system with the following outcomes:

• Energy Cost Reduction: Identified strategies that helped households reduce energy costs by an average of 15%.

• Proactive Alerts: Enabled alerts for high-consumption households, promoting timely interventions.

• Peak Usage Insights: Provided actionable insights on peak-hour consumption, aiding energy providers in load balancing.

• Improved Awareness: The dashboard educated users on their energy consumption habits, encouraging sustainable practices.

This project demonstrated the potential of data-driven insights in promoting efficient and eco-friendly energy usage.

Git Link

For more information and code, visit the Git link.