Smart Energy Optimization Agent (SEOA)

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  System Architecture of the Smart Energy Optimization Agent (SEOA)

Overview

Smart Energy Optimization Agent (SEOA) is a proposed AI + IoT system designed to monitor, predict, and optimize building energy consumption in real time. This concept is being submitted for the “AI for Energy Saving” problem under the SabkaAI: AI for Inclusion – Innovation Challenge 2026. The solution focuses on sustainability, cost reduction, and inclusive access through lightweight, scalable technology.

Inspiration

The idea is motivated by rising energy wastage in homes and institutions, lack of real-time visibility into consumption, and the absence of predictive tools for energy planning. With increasing electricity bills and growing environmental concerns, the motivation is to design a system that makes energy optimization affordable and accessible for everyone.

What the solution will do

• Monitor real-time energy usage using smart meter / IoT data
  
• Predict next-hour and next-day energy consumption
  
• Detect anomalies or sudden spikes
  
• Generate personalized, actionable energy-saving recommendations
  
• Estimate cost savings based on usage patterns
  
• Run efficiently on low-cost edge devices such as Raspberry Pi
  

How we plan to build it

• IoT data capture through ESP32 / smart meter integration
  
• Data ingestion pipeline using MQTT and REST APIs
  
• Time-series forecasting models (LSTM, GRU, Prophet)
  
• Anomaly detection using Isolation Forest / Autoencoders
  
• Hybrid recommendation engine combining rule-based and ML techniques
  
• Dashboard using React / Streamlit for easy accessibility
  
• Edge inference optimization for lightweight devices to ensure low latency
  

Expected challenges

• Handling noisy or inconsistent smart meter data
  
• Achieving accurate forecasting with small or incomplete datasets
  
• Ensuring low-latency inference on edge hardware
  
• Designing a scalable architecture for homes, campuses, and offices
  
• Making recommendations clear and usable for non-technical users
  

What we aim to learn

• Effective time-series modeling for energy datasets
  
• Practical IoT-to-AI integration workflows
  
• Edge AI model compression and optimization
  
• Data preprocessing techniques for sensor-based inputs
  
• Designing accessible and intuitive UIs for diverse users
  

Future Scope

• Solar panel optimization and integration
  
• Smart-grid feedback and demand-response automation
  
• Occupancy-aware energy usage controls
  
• Voice-based control (Hindi / Punjabi)
  
• Carbon footprint tracking and reporting
  
• Automated appliance control with IoT integrations
  

Built With

• amazon-web-services
• c++
• esp32
• http-rest
• influxdb
• mongodb
• mqtt
• prophet
• python
• pytorch
• raspberry-pi
• react
• rust
• scikit-learn
• streamlit