The startup develops cloud-based machine learning software that optimizes control strategies for chilled water plants in commercial air conditioning and refrigeration systems. This technology enhances operational efficiency and sustainability, enabling building owners and service providers to significantly reduce energy consumption and operational costs.
Funding
$790K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Commercial buildings consume a significant amount of energy, with chilled water plants accounting for a substantial portion of this consumption. Optimizing the performance of these complex systems is challenging due to the numerous variables and interdependencies involved. Traditional methods for managing these systems often fall short of achieving peak efficiency, leading to increased energy consumption and operational costs.
Solution
Exergenics offers a cloud-based machine learning platform that optimizes control strategies for chilled water plants, enhancing operational efficiency and sustainability. The platform leverages existing building data to provide best-in-class control strategies, programmed directly into the existing Building Management System (BMS). By creating an AI digital twin of the central plant, the software simulates operational performance and identifies thermodynamically optimal controls based on real-world performance, load, and weather profiles. This approach enables building owners and service providers to reduce energy usage, lower carbon emissions, and improve the performance and lifespan of plant equipment. Exergenics integrates with existing building infrastructure, requiring no additional hardware and enabling quick and seamless rollout across building portfolios.
Target Audience
Exergenics primarily serves building owners, facility managers, and service providers responsible for managing and optimizing the energy consumption of commercial buildings with chilled water plants.
Features
- Cloud-based machine learning tool for chilled water plant optimization
- AI Digital Twin technology that simulates plant operations using historical BMS data
- API integration with existing data warehouses and Building Management Systems (BMS)
- Patented Pareto Optimizer to balance mechanical performance and energy efficiency
- Automated measurement and verification of energy savings and carbon abatement
- Retrofit simulation tools for accurate forecasting of energy and cost impacts of large-scale upgrades
- Ability to identify poorly calibrated sensors using Energy-Mass Balance analytics
- Integration with existing building infrastructure, requiring no additional hardware