HVAC Energy Optimization Under Variable Ventilation Conditions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing energy optimization solutions for building HVAC systems are unable to account for variations in ventilation rates, which are necessary to ensure air circulation and reduce the risk of airborne diseases, leading to a conflict with energy efficiency goals.
Innovation Solution
A computer-implemented method and system that collects data from field sensors, calculates aggregated ventilation rates, and uses predictive and baseline models to optimize energy use, incorporating variable ventilation rates into the optimization process, ensuring energy savings without compromising occupant comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ventilation rate is increased to ensure air circulation and reduce airborne disease risk, then occupant safety and air quality are improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts damper positions and ventilation rates based on real-time occupancy detection and predictive models, transitioning from static minimum ventilation to adaptive variable ventilation that responds to actual building conditions and forecasted occupancy patterns
Solution Approach 2:
The system uses predictive models to anticipate future occupancy and ventilation needs, pre-adjusting damper positions and HVAC operations in advance based on forecasted conditions, allowing optimization before the actual ventilation demand occurs
2Productivity
If ventilation rate is varied in response to occupancy rates, then air circulation effectiveness is improved, but existing energy optimization solutions become inadequate
Solution Approach 1:
The system integrates multiple functions into a unified platform that combines occupancy detection, predictive modeling, real-time optimization, and HVAC control, enabling the system to handle variable ventilation requirements while maintaining energy optimization capabilities that were previously unavailable
Solution Approach 2:
The system implements continuous feedback loops where occupancy sensors provide real-time data to predictive models, which then adjust damper positions and ventilation rates, with performance monitored and refined through ongoing data collection and model updates
Data Source
AI summary
Systems and methods for energy optimization of an HVAC system of a building are disclosed. The method includes collecting data from field sensors, the data including damper positions of a plurality of air handling units; calculating an aggregated ventilation rate for the air handling units based on the damper positions; retrieving a predictive model that outputs a predicted state for a plurality of building zones; inputting the damper positions to the predictive model; retrieving a baseline model that outputs an expected energy cost for a reporting period; inputting the aggregated ventilation rate to the baseline model; performing batch data analytics on the predictive model to update a building model; optimizing energy use to minimize actual energy cost based on the building model, energy cost information, and the data collected from the field sensors; generating energy savings data based on the baseline model and the actual energy cost.