IoT-Based Energy Monitoring & Optimization
We developed an intelligent IoT system for energy consumption monitoring that provides device-level disaggregation and predictive maintenance alerts using custom sensors, Raspberry Pi edge devices, and Azure cloud services.
Reduced energy consumption
23%
Decreased maintenance costs
$275K annually
Improved equipment lifespan
31%
Technologies Used
Raspberry Pi
Azure IoT
Machine Learning
Custom Sensors
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Contact usThe Challenge
Our client was managing multiple facilities with high energy consumption but lacked visibility into device-specific usage patterns. Their energy monitoring was limited to building-level metrics, making it impossible to identify inefficient equipment or predict potential failures. This resulted in excessive energy costs, unexpected equipment breakdowns, and difficulty implementing targeted optimization strategies.
Our Solution
We architected a comprehensive IoT solution featuring custom energy monitoring sensors connected to a cloud-based analytics platform. The system leverages edge computing with Raspberry Pi devices for local processing and an Azure-hosted platform for advanced analytics. Our AI models perform energy disaggregation to identify consumption at the device level and detect anomalous patterns that indicate potential maintenance issues before they cause failures.
System Architecture
Custom IoT sensors → Edge Processing → Azure Cloud → AI Analytics → Predictive Insights
Implementation Approach
Designed and deployed custom IoT sensor arrays to monitor energy consumption across various equipment types
Implemented an edge computing network using Raspberry Pi devices for real-time data processing and local analytics
Built a scalable data pipeline on Azure IoT Hub with globally accessible storage instances
Developed AI models for energy disaggregation and anomaly detection to enable predictive maintenance
Created comprehensive dashboards providing actionable insights on energy usage patterns and optimization opportunities
Outcome
The energy monitoring system has transformed how our client manages their facilities. By implementing device-level energy disaggregation, they now have unprecedented visibility into consumption patterns and can immediately identify inefficient equipment. The predictive maintenance alerts have prevented numerous potential failures, extending equipment lifespan significantly. We continue to support them with data flow optimization, architecture improvements, and enhanced AI capabilities as part of an ongoing upgrade path.
ROI Achieved
Ready to optimize your energy consumption?
Let's discuss how our IoT solutions can help monitor energy usage, reduce costs, and prevent equipment failures.