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IoT-Based Energy Consumption Optimization System for Manufacturing Plants

yalantis

Manufacturing

Agriculture

Challenges in Energy Management and Sustainability

The client faced high energy consumption costs, lack of real-time visibility into equipment-specific energy usage, and pressure to meet sustainability goals. Existing systems failed to provide actionable insights for reducing waste or enabling predictive maintenance.

About the Client

Large US-based manufacturer specializing in agricultural machinery components, prioritizing environmental sustainability and operational efficiency.

Goals for Energy Efficiency and Operational Improvement

Reduce energy consumption by 30% through real-time monitoring and analytics
Decrease energy costs by 25% within the first operational year
Implement predictive maintenance to minimize downtime and optimize resource allocation
Develop a scalable IoT system for real-time energy data aggregation and visualization

Core System Functionalities

Integration with IoT sensors (power, current, voltage) via MQTT protocol
Real-time energy consumption dashboard for plant-wide and machine-specific monitoring
Predictive maintenance module using ML algorithms for anomaly detection
Historical data storage and trend analysis for demand planning
Multi-platform notifications for abnormal energy usage or equipment malfunctions

Technology Stack

IoT Accelerator framework

MQTT protocol for data transmission

ESP8266/ESP32 WiFi modules

Cloud-based MQTT broker

Time-series database for sensor data storage

System Integrations

Legacy manufacturing equipment APIs
Enterprise cloud platforms (AWS/Azure)
ERP systems for cost and resource planning

Non-Functional Requirements

Scalability to support 10,000+ IoT sensors
Real-time data processing with <1s latency
End-to-end encryption for sensor data security
99.9% system uptime for critical monitoring functions

Expected Business Impact

Projected 30% reduction in energy consumption and 25% lower utility costs within 12 months, enhanced sustainability leadership through real-time environmental impact tracking, and improved operational efficiency via predictive maintenance reducing unplanned downtime by 40%.