Create a customized plan based on your facility goals and data – automatically. We start with your priorities, goals and constraints for managing the facility. Next, Callida’s software learns energy use and occupancy patterns.
A plan tailored to your facility using a big data approach. Your priorities drive the trade-offs made in delivering the optimal building control solution. You choose which of the Callida solutions to deploy in your facility:
Callida Occupant App Solution
The Callida Occupant App solution turns off cooling or heating to unoccupied space to stop energy waste. Occupants use the Callida Occupant App to submit real-time updates on office comfort. The Callida platform uses app inputs to learn occupancy patterns, create realistic schedules and identify changes to thermal set points to drive down waste and energy costs. Zone comfort problems are visualized for facilities management to improve occupant comfort.
Callida Monitoring Solution
Callida monitors demand to avoid setting new peaks and to determine ways to reduce costly demand charges. Callida monitors energy consumption to point out opportunities for reductions and to verify savings achieved.
Callida Predictive Control Solution
The Callida Predictive Control solution uses predictive models of the building energy consumption and space temperature to recommend improved HVAC control strategies that reduce energy use and costs while maintaining occupant comfort.
A hidden opportunity exists within collected building sensor and meter data to achieve comfort objectives with less waste. Our solution unlocks the power of your data, looking for patterns, detecting inefficiencies and creating new opportunities to reduce energy costs and carbon. Callida works with you to build a strong data foundation to drive improvement in your facility performance today and tomorrow.
Demand charges based on your sustained peak demand can comprise 30% to 70% of your facility’s electric bill. Callida helps you attack your peaks and deliver savings from reduced demand charges and lower consumption. Peak demand reductions also increase your opportunity to participate in demand response programs.
Engage occupants by letting them submit real-time information through the Callida app. Use that data to drive down waste by ensuring that conditioning is delivered only where people are present. Improve comfort as the Callida system learns actual occupancy patterns and provides office workers a way to give comfort input with one click.
Callida software learns energy usage patterns and takes into account your facility goals, priorities and constraints to recommend the optimal building control strategy. We automate energy optimization to minimize the manual effort of developing a customized solution that can be continuously improved.
Use learned occupancy patterns and real-time updates to turn-off A/C and heat to unoccupied office space to cut energy waste. Utilize Callida’s predictive control approach to obtain a 24-hour building control strategy of set points and schedules to manage HVAC systems as controlled by the building management system (BMS). This strategy utilizes the building’s energy dynamics, weather forecasts, occupancy, energy prices, facility input and learned usage patterns to deliver an optimized control strategy for every day of the year.
Monitor operations and verify savings compared to your baseline. Use Callida to monitor building operations and track energy consumption and peak demand. Our solution monitors utility usage to prevent setting new demand peaks and to point out opportunities for reductions. We create a baseline of facility energy usage, which is used to verify savings achieved through Callida recommended building control strategies. Normalized energy usage is provided to allow accurate comparisons of current results vs. historical performance. Automatically check air handling unit compliance with the intended control strategy to ensure that units that should be off – are turned off.
Predictive control is the next step in optimizing energy usage in buildings. We use a machine learning approach to create a data-driven model of energy use based on building and utility data. Callida’s software uses this model to predict energy requirements based on weather forecasts, projected occupancy and comfort constraints. The optimal control strategy is created by using unique facility input on goals, constraints and priorities to optimize energy forecasts, automatically. Through our adaptive approach we also close the loop for continuous improvement.