Universal Energy Management Platform

UEMP is an AI-driven platform designed to revolutionize energy management in telecom, enterprise and verticals. It utilizes advanced artificial intelligence and machine learning technologies to optimize energy consumption and enhance operational efficiency. Customer consumes Stellar Sage as SaaS or on-prem solution based on their obligations.

Today, industries face several challenges regarding energy management:

• Rising Energy Costs: Energy prices continue to rise, creating a financial burden for enterprises and service providers.

• Sustainability Mandates: Increasing pressure from governments and consumers to reduce carbon emissions and improve environmental sustainability.

• Operational Inefficiencies: Legacy systems and disconnected energy infrastructures lead to energy waste, increased downtime, and inefficient operations.

• Complex Energy Grids: Modern operations often require managing multiple energy sources, including renewable and traditional energy, which complicates energy distribution and management.

• Data Silos: Lack of unified data makes it difficult to have a holistic view of energy usage across different locations, devices, and infrastructures.

U-EMP addresses these challenges by delivering a comprehensive, data-driven platform that provides real-time control, efficiency optimization, and predictive analytics.

Industry’s Current Challenges

AI module flow diagram in the UEMP represents the architecture and flow of how data and decisions are managed within the platform's AI-driven system for energy management.

• Predictive Models: These models forecast energy demand, system load, and environmental conditions. For example, the platform might predict peak energy consumption times or battery degradation trends.

• Optimization Algorithms: Based on the predictions, the platform runs optimization algorithms to recommend the most efficient use of energy. For example, optimizing HVAC systems to minimize energy use while maintaining comfort levels or shifting loads to batteries during peak grid pricing.

• Reinforcement Learning: In more advanced setups, reinforcement learning allows the system to "learn" optimal energy-saving actions based on past performance and adapt over time to changing conditions.

• Anomaly Detection: The AI constantly monitors for any deviations from normal operating patterns, such as unusually high energy consumption or equipment failure, allowing for proactive management.

UEMP : Some Use Cases

AI Modules in UEMP

1. HVAC Energy Management and Optimization

UEMP enables real-time monitoring and AI-driven control of HVAC systems to optimize energy consumption. By analyzing occupancy levels, temperature data, and external conditions, UEMP reduces unnecessary energy use, lowering operational costs and improving efficiency.

2. Battery Management

The platform provides intelligent management of battery systems by predicting energy demand and automating charge/discharge cycles. This extends battery lifespan, ensures optimal performance, and offers cost-saving opportunities during peak energy periods

3. Peak Shaving

UEMP helps telecom operators and enterprises reduce peak energy demand by shifting loads and utilizing stored battery energy during high-cost periods. This reduces energy expenses, avoids penalties, and stabilizes grid demand.

4. Carbon Connect Suits

UEMP’s Carbon Connect Suite integrates sustainability goals by tracking and managing carbon emissions in real-time. It provides actionable insights to minimize carbon footprint, aligning energy usage with environmental targets and reporting progress on sustainability initiatives.

UEMP Benefits