17933236. ENERGY OPTIMIZATION OF HVAC SYSTEMS UNDER VARIABLE VENTILATION CONDITIONS simplified abstract (HONEYWELL INTERNATIONAL INC.)
Contents
- 1 ENERGY OPTIMIZATION OF HVAC SYSTEMS UNDER VARIABLE VENTILATION CONDITIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ENERGY OPTIMIZATION OF HVAC SYSTEMS UNDER VARIABLE VENTILATION CONDITIONS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
ENERGY OPTIMIZATION OF HVAC SYSTEMS UNDER VARIABLE VENTILATION CONDITIONS
Organization Name
Inventor(s)
ENERGY OPTIMIZATION OF HVAC SYSTEMS UNDER VARIABLE VENTILATION CONDITIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17933236 titled 'ENERGY OPTIMIZATION OF HVAC SYSTEMS UNDER VARIABLE VENTILATION CONDITIONS
Simplified Explanation
The abstract describes systems and methods for energy optimization of an HVAC system in a building. Here is a simplified explanation of the patent application:
- Collect data from field sensors, including damper positions of air handling units.
- Calculate an aggregated ventilation rate based on the damper positions.
- Use a predictive model to predict the state of building zones.
- Input damper positions to the predictive model.
- Retrieve a baseline model for expected energy cost.
- Input the aggregated ventilation rate to the baseline model.
- Update a building model through batch data analytics on the predictive model.
- Optimize energy use to minimize actual energy cost based on the building model and data from field sensors.
- Generate energy savings data based on the baseline model and actual energy cost.
Potential Applications
The technology can be applied in commercial buildings, industrial facilities, and residential complexes to optimize energy use and reduce costs.
Problems Solved
This technology addresses the challenge of efficiently managing energy consumption in HVAC systems, leading to cost savings and improved sustainability.
Benefits
The benefits of this technology include reduced energy costs, improved comfort levels in buildings, and increased energy efficiency.
Potential Commercial Applications
The technology can be used in smart buildings, energy management systems, and HVAC control systems to optimize energy usage and reduce operational expenses.
Possible Prior Art
One possible prior art in this field is the use of building automation systems to control HVAC systems for energy efficiency. Another could be the integration of predictive modeling in energy management systems for buildings.
What are the specific predictive models used in this technology?
The specific predictive models used in this technology are designed to output a predicted state for a plurality of building zones based on input data such as damper positions of air handling units.
How does this technology compare to traditional HVAC energy optimization methods?
This technology differs from traditional HVAC energy optimization methods by utilizing predictive modeling, batch data analytics, and building models to optimize energy use and minimize costs in a more efficient and effective manner.
Original Abstract Submitted
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.
