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wattwise-ai home page
Inspiration
WattWise AI was inspired by a real and recurring problem I observed in Indian industries: massive energy wastage despite the availability of detailed consumption data. During my interactions with manufacturing units, I noticed that energy usage was often tracked manually using spreadsheets, anomalies were detected only after high electricity bills arrived, and professional energy audits were expensive and infrequent.
A defining moment came when a factory manager showed me a monthly electricity bill running into lakhs of rupees and asked a simple question: “We have all this data, but how do we actually use it to save energy?” This highlighted a clear gap between data collection and actionable intelligence, which became the foundation for building WattWise AI.
What it does
WattWise AI is an intelligent energy analysis and optimization platform that converts raw energy consumption data into clear, actionable insights.
Key capabilities include:
Automated detection of abnormal energy consumption and spikes
Department, equipment, and building-level energy analysis
AI-powered recommendations using Google Gemini for energy optimization
Historical comparison of uploads to track improvements over time
Business-focused insights with estimated cost savings in INR
The platform enables organizations to move from reactive energy management to proactive, data-driven decision-making.
How I built it
I built WattWise AI using a modular and scalable architecture:
Technology Stack
Backend: Python 3.11, Flask, SQLAlchemy
Frontend: Jinja2 templates, Bootstrap 5, JavaScript
Database: SQLite
AI Integration: Google Gemini API with intelligent fallback logic
Security: Flask-Login, CSRF protection, secure API key handling
Development Approach
Designed a structured database schema for users, energy data, insights, and AI recommendations
Implemented CSV upload with validation and chunked processing for large datasets
Built statistical models for anomaly detection and trend analysis
Integrated Gemini AI to generate contextual recommendations
Developed dashboards, filtering, and upload comparison features
Focused on usability with clean UI, error handling, and responsive design
Challenges I ran into
Accurate anomaly detection: I fixed false positives by using dynamic baselines and statistical Z-score analysis instead of static thresholds.
AI reliability and API handling: I implemented a smart fallback system to ensure the platform works even when AI services are unavailable.
Large CSV file processing: I solved memory issues by using chunk-based data processing and optimized database inserts.
Data format inconsistencies: I built a flexible CSV parser to handle variations in real-world energy datasets.
Translating technical data into business value: I addressed this by presenting insights in terms of cost impact and potential savings.
Accomplishments that I'm proud of
Built a complete, end-to-end energy intelligence platform from scratch
Achieved high accuracy in detecting energy anomalies with minimal false alerts
Reduced energy analysis time from days to minutes
Delivered AI-powered recommendations with clear business relevance
Designed a resilient system with graceful handling of failures
Created a solution tailored specifically for Indian industrial contexts
What I learned
Raw data alone is not valuable — insights and interpretation drive action
AI works best as an assistant that augments human decision-making
Domain understanding is critical for meaningful recommendations
Simplicity and clarity are essential for user adoption
Designing for reliability is as important as adding advanced features
What's next for WattWise AI
Planned future enhancements include:
Mobile applications for real-time alerts and monitoring
Predictive analytics for equipment failure and energy forecasting
Integration with IoT energy meters for live data ingestion
Automated reporting and export features
Industry benchmarking and ESG-focused analytics
My long-term vision is to make WattWise AI a comprehensive energy intelligence platform that helps organizations reduce costs, improve efficiency, and move toward sustainable operations.
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