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Optimizing Manufacturing Energy Consumption with Real-Time IoT Sensor Analytics on Azure Digital Twins

Energy Consumption

Energy Consumption with Real-Time IoT Sensor Analytics on Azure

  • Use Case:

    Manufacturing plants often face high energy costs and inefficient energy usage due to lack of real-time visibility into machine performance, environmental conditions, and operational workflows.

    This use case demonstrates how IoT sensors integrated with Azure Digital Twins can provide a virtual replica of manufacturing operations, enabling real-time monitoring, predictive analytics, and energy optimization.

Objective

  • To build a real-time digital twin of manufacturing assets (machines, HVAC, lighting, robotics, etc.).

    To collect IoT sensor data (temperature, vibration, energy usage, etc.) and correlate with operational workflows.

    To apply predictive analytics for detecting inefficiencies and recommend energy-saving strategies.

    To optimize energy consumption while maintaining production efficiency and reducing costs.

Project Description

  • The project creates a Digital Twin environment of a smart factory by integrating IoT devices, energy meters, and equipment sensors with Azure Digital Twins. Data from sensors is ingested via Azure IoT Hub, processed with Stream Analytics, and stored in Azure Data Lake for long-term analysis.

    Azure Digital Twins models the entire manufacturing floor, including machines, devices, and energy systems, providing a graph-based digital representation. Real-time analytics and AI/ML models (Azure Machine Learning) are applied to predict peak energy usage, detect anomalies, and suggest optimizations (e.g., load shifting, predictive maintenance, energy-efficient scheduling).

    Visualization and reporting are enabled through Power BI dashboards, giving plant managers actionable insights to reduce energy costs by up to 20–30% while ensuring production quality.
  • Azure Services Used :
    Azure Service Purpose
    Azure Digital Twins Create virtual replicas of manufacturing assets and energy systems.
    Azure IoT Hub Connect and manage IoT devices and collect sensor data.
    Azure Stream Analytics Real-time processing and analysis of sensor streams.
    Azure Data Lake Storage Store raw and processed energy data for long-term analysis.
    Azure Machine Learning Build ML models for energy optimization and predictive maintenance.
    Azure Time Series Insights Analyze historical IoT sensor data for trend detection.
    Azure Event Hub Event ingestion at scale from IoT devices and sensors.
    Azure Functions Automate energy optimization rules and event-driven workflows.
    Power BI Interactive dashboards for energy consumption insights.