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Machine Learning-Based Energy Optimization in Smart Homes using AWS IoT and SageMaker Edge

Smart Homes using AWS

Energy Optimization in Smart Homes using AWS

  • Use Case: Smart homes often consume energy inefficiently due to unoptimized device usage patterns.This project enables real-time energy optimization by monitoring appliance behavior and predicting optimal usage schedules using ML at the edge.

Objective

  • To minimize energy consumption in smart homes.

    To implement real-time prediction and control of IoT-enabled devices using edge ML models.

    To reduce cloud inference costs by leveraging on-device (edge) prediction using SageMaker Edge.

Project Description

  • This project creates an intelligent energy management system for smart homes by deploying a machine learning model to edge devices using AWS SageMaker Edge Manager. Here's the complete pipeline:

Data Collection

  • Use AWS IoT Core to connect smart home sensors and appliances (temperature, humidity, motion, smart plugs, etc.).

    Collect real-time energy usage and device activity data into Amazon Timestream or Amazon Kinesis Data Firehose.

Model Training

  • Train ML models in Amazon SageMaker to forecast device-level energy usage patterns.

    Use SageMaker Experiments and Hyperparameter tuning for optimizing accuracy and cost-efficiency.

Edge Deployment

  • Compile and deploy models to edge gateways using SageMaker Neo and SageMaker Edge Manager.

    The edge device makes real-time predictions to optimize device usage (e.g., turning off HVAC when no one is home).

Inference and Action

  • The edge gateway triggers actions like reducing power, shutting off unused devices, or alerting users.

    Use AWS IoT Greengrass for local device orchestration and control logic.

Monitoring and Feedback Loop

  • Use Amazon CloudWatch and AWS IoT Device Defender to monitor device performance and security.
  • Upload results back to SageMaker for continuous model improvement.
  • Key Technologies & AWS Services :
    Category AWS Services
    Data Collection & Processing AWS IoT Core, AWS IoT Analytics, Amazon Kinesis, AWS Lambda
    Model Training & Management Amazon SageMaker, SageMaker Experiments, SageMaker Hyperparameter Tuning
    Edge Deployment SageMaker Edge Manager, SageMaker Neo, AWS IoT Greengrass
    Storage Amazon S3, Amazon Timestream
    Monitoring & Security AWS CloudWatch, AWS IoT Device Defender, AWS Identity and Access Management (IAM)
    Automation & Notifications AWS Lambda, Amazon SNS