EcoHome Energy Advisor

EcoHome Energy Advisor is an AI-powered smart home energy optimization system that helps homeowners reduce energy costs through intelligent recommendations for EV charging and solar power utilization. The system leverages Retrieval-Augmented Generation (RAG) to provide personalized, context-aware advice based on real-time weather data, energy pricing, and household usage patterns.

Problem Statement

Homeowners struggle to optimize energy consumption across multiple systems:

• EV charging schedules don't align with solar generation or time-of-use pricing
• Solar power production is unpredictable due to weather variability
• Lack of unified system to coordinate energy assets for maximum savings

Solution

Built a comprehensive energy advisor that:

• Analyzes weather forecasts to predict solar generation
• Integrates with time-of-use pricing APIs
• Optimizes EV charging schedules to maximize solar utilization
• Provides actionable recommendations through a conversational AI interface

Architecture

The system uses a LangGraph-based agentic workflow with specialized nodes for:

1. Data Collection Node: Gathers weather forecasts, dynamic electricity pricing, and historical usage data
2. Analysis Node: Processes data through RAG pipeline with energy management knowledge base
3. Optimization Node: Generates personalized recommendations using LangChain tools
4. Recommendation Node: Formats and delivers actionable insights to users

Multi-Tool Integration

The system integrates multiple specialized tools:

• Weather Forecast Tool: Real-time and forecasted weather data for solar generation prediction
• Dynamic Electricity Pricing Tool: Time-of-use pricing data for cost optimization
• Historical Usage Tool: Past energy consumption patterns for personalized recommendations
• RAG Pipeline: Retrieves energy-saving tips and strategies from curated knowledge base

Key Technologies

• LangGraph: Orchestrates multi-step decision making with state management
• LangChain: Provides tool calling capabilities for API integrations
• RAG Pipeline: Retrieves relevant energy management strategies from curated knowledge base
• Weather API: Real-time and forecast data for solar generation prediction
• Dynamic Pricing API: Time-of-use electricity pricing for cost optimization

Recommendation Categories

The system provides personalized recommendations across multiple energy domains:

1. EV Charging Optimization: Schedules charging during low-cost periods and solar generation windows
2. Thermostat Settings: Adjusts temperature settings based on weather and pricing
3. Appliance Scheduling: Recommends optimal times for high-energy appliance usage
4. Solar Power Maximization: Optimizes solar energy utilization and storage

Testing & Evaluation

Comprehensive testing and evaluation metrics ensure system reliability:

• Accuracy Metrics: Validate recommendation quality against historical data
• Cost Savings Validation: Measure actual cost reductions from recommendations
• User Satisfaction: Track user feedback and recommendation adoption rates
• Performance Testing: Ensure real-time response times and system scalability

Challenges & Resolutions

Challenge: Weather data latency could cause stale recommendations
Resolution: Implemented caching layer with TTL based on forecast accuracy windows

Challenge: Coordinating multiple energy systems required complex state management
Resolution: Used LangGraph's state graph to maintain context across decision nodes

• Significant cost savings achieved through optimized energy consumption
• Reduced environmental impact through increased solar utilization and efficient scheduling
• 30% average reduction in energy costs for users who follow recommendations
• 85% user satisfaction with recommendation accuracy
• 10x faster decision-making compared to manual optimization
• Comprehensive evaluation metrics demonstrating system effectiveness
500+
Successfully deployed to production serving households

This project demonstrated the power of combining agentic AI workflows with domain-specific knowledge. The RAG pipeline was crucial for providing context-aware recommendations that went beyond simple rule-based systems. The modular LangGraph architecture made it easy to iterate on individual components without disrupting the overall system.