A chilled water plant is very energy intensive and will typically account for 25% of a commercial building's total electricity consumption.
Each piece of equipment in this system has varying efficiencies based on a number of factors, as well as interdependencies with other equipment within the system, making it very challenging for humans to optimise.
To address this challenge, Exergenics’ novel optimisation methodology for chilled water plants was born. Core to Exergenics’ novel approach is the proprietary modelling and simulation algorithms that can find the precise combination of setpoints that maximise efficiency and mechanical performance out of the millions of possible combinations of setpoints.
Exergenics is designed to leverage investments in data warehousing via API integration, to ensure a simple and transparent experience for building owners and operators, enabling rapid rollouts across portfolios, without the need for any new hardware or technicians.
- The forecasting of energy savings, cost, and environmental impact before commissioning.
- Automating verification of strategy deployment, while automatically measuring & verifying energy savings.
- Tracking and benchmarking performance at an equipment, system, and portfolio level.
Key points of differentiation:
- Solution prioritises transparency to provide full visibility to building operators or service providers into current performance, and generates AI-driven control strategy recommendations with forecasted energy savings.
- Easy to implement, their cloud-based optimisation engine leverages existing data from a building’s BMS system or Datalake, and produces control strategy update recommendations in a simple format that could be implemented seamlessly by existing BMS providers without any CAPEX, additional hardware or new technicians.
- Our cloud-based, AI-driven model enables us to develop tailored chiller plant control strategy updates at scale, while still incorporating site knowledge to maximise impact.
- Being cloud based, AI driven & software only, Exergenics’ cost to serve is significantly lower than labour intensive methods currently available. This allows Exergenics to consistently achieve payback periods of under 12 months for clients.//ML is utilised in our solution into two essential stages of our process.
1- Utilising our algorithms to build the mathematical digital twin model based on the acquired historical data from a chilled water plant.
2- Running simulations on the digital twin using optimisation algorithms to minimise the total energy consumptions of chilled water plant equipment by maximising the whole system (central plant) coefficient of performance, under a known discrete cooling load and constrained within mechanical performance and occupant comfort parameters.
Water Plant Energy Savings