AI-powered Energy Optimisation Solution
India
Environment
Implementing Organisation
Zodhya
India, India
Implementing Point of Contact
Rohith
Founder & CEO
Contributor of the Impact Story
International Energy Agency
Year of implementation
2026
Problem statement
Commercial buildings face significant energy inefficiency because HVAC systems are designed to handle peak conditions but operate under partial load most of the time, while static control systems fail to adapt dynamically to factors such as varying occupancy or weather conditions. This creates widespread inefficiencies in temperature settings, overcooling, excessive cycling, and energy losses that typically drive 20-40% of excess energy consumption. Building managers lack tools to optimize energy use continuously or assess system performance in real time, with periodic audits and retro-commissioning exercises being expensive, slow, and unable to provide long-term solutions. The problem is worsened by aging infrastructure and the risk-averse nature of the industry where upfront capital expenditures are avoided, leaving buildings operating inefficiently while contributing to high costs and excess emissions.
Submission Overview
Zodhya operates as a provider of retrofit-first, AI-powered energy optimization solutions for commercial buildings. The company integrates AI into existing electrical infrastructure to deliver scalable, cost-effective energy savings without requiring costly infrastructure replacements.
AI Technology Used
Key Outcomes
Zodhya's retrofit-first AI platform successfully addresses a fundamental inefficiency in commercial buildings where Heating, Ventilation, and Air Conditioning (HVAC) systems designed for peak loads operate mostly at partial capacity with static controls unable to adapt to real-world variations in occupancy and weather. By deploying SaverX for HVAC optimization and Soul for electrical performance analytics as a cloud-edge hybrid system, Zodhya eliminates the need for costly infrastructure replacement while continuously monitoring and adjusting parameters like chilled water set points, air supply temperatures, and operating schedules based on real-time data. The AI and machine learning models adapt to each building's unique operational profile, proactively optimizing systems in response to demand forecasts and environmental changes to avoid unnecessary cooling, reheating, and energy losses without compromising occupant comfort.
Impact Metrics
Overall energy consumption in commercial buildings equipped with SaverX and Soul systems.
Baseline Value
NA
Post-Implementation
Energy consumption decreased by 25-30% across more than 50 buildings in India and Southeast Asia.
Monthly CO₂ emissions reduction from optimized HVAC and electrical systems.
Baseline Value
NA
Post-Implementation
Over 1,000 tonnes of CO₂ emissions are avoided monthly across deployed buildings.
Implementation Context
Over 50 buildings across India and South Asia
Key Partnerships
Replicability & Adaptation
1. The solution is implemented without replacing existing equipment" Works with aging infrastructure, eliminating the need for costly capital expenditures that risk-averse industries typically avoid. 2. Deployed as a cloud-edge hybrid system, enabling real-time decision-making on-site while benefiting from cloud-based analysis for continuous improvement. 3. Can be implemented quickly without major construction or downtime. 4. System adapts to each building's unique operational profile (not one-size-fits-all)
* The data presented is self-reported by the respective organisations. Readers should consult the original sources for further details.