Predictive HVAC Conditioning Using Occupancy Pattern Detection
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Solution Overview
Problem
Conventional HVAC systems in residential and small commercial buildings often inefficiently condition entire spaces regardless of occupancy, leading to energy wastage and discomfort due to uneven temperature distribution and the need for frequent temperature adjustments, as they rely on a single thermostat and lack predictive occupancy sensing.
Innovation Solution
A system that detects and predicts occupancy in multiple zones using sensors and databases, adjusting environmental conditions based on historical patterns, occupant class and attributes, and external weather forecasts to proactively set optimal temperatures before occupancy, thereby reducing energy consumption and enhancing comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If the entire building space is conditioned irrespective of actual occupancy, then the temperature is maintained uniformly throughout the building space, but energy is wasted by conditioning unoccupied areas
Solution Approach 1:
The building space is divided into multiple occupancy zones with individual thermostats and sensors. Each zone is conditioned independently based on its own occupancy status, allowing the system to maintain temperature uniformity in occupied areas while avoiding energy waste in unoccupied areas.
Solution Approach 2:
Different parts of the building space are conditioned according to their local occupancy conditions. Occupied zones receive full conditioning while unoccupied zones are left unconditioned or minimally conditioned, creating local variations in temperature control that match actual usage patterns.
2Ease of operation
If a single thermostat controls the building space temperature, then the system is simple to operate, but the temperature distribution becomes uneven and requires frequent adjustments
Solution Approach 1:
The single thermostat is replaced with multiple zone thermostats distributed throughout the building space. Each thermostat controls the temperature in its specific zone, enabling independent temperature adjustment that achieves uniform distribution without requiring frequent manual interventions.
Solution Approach 2:
Occupancy sensors provide real-time feedback about zone occupancy status to the control system. This feedback enables automatic adjustment of temperature settings in each zone based on actual occupancy, maintaining uniform temperature distribution without requiring user intervention for frequent adjustments.
3Loss of energy
If programmable thermostats adjust temperature based on programmed settings, then energy efficiency is improved, but the system requires complex programming configurations that users find difficult to operate
Solution Approach 1:
The system automatically detects occupancy patterns using sensors and uses this information to adjust temperature settings without requiring user programming. The system serves itself by learning and adapting to occupancy patterns, achieving energy efficiency while maintaining ease of operation.
Solution Approach 2:
The system predicts future occupancy based on historical patterns and proactively adjusts temperature settings in advance of predicted occupancy events. This preliminary action maintains energy efficiency by pre-conditioning spaces before occupancy occurs, eliminating the need for complex user programming.
4Loss of energy
If the building space is not conditioned when occupants are away, then energy consumption is reduced, but occupants experience discomfort upon return while waiting for conditioning
Solution Approach 1:
The system predicts when occupants will return based on historical occupancy patterns and proactively conditions the building space in advance of their arrival. This preliminary conditioning action ensures comfort upon return while minimizing energy consumption by conditioning only when and where needed.
Solution Approach 2:
The system dynamically adjusts temperature settings based on real-time occupancy detection and predicted future occupancy. Temperature control transitions from static programmed settings to dynamic responsive control that adapts to actual occupancy patterns, reducing both energy consumption and wait time for conditioning.
Data Source
AI summary
A method and system for predictively controlling environmental conditions of multiple occupancy zones in an occupancy space are provided. Occupancy and environmental conditions of each of the occupancy zones are detected and identified. The detected occupancy is stored in an occupancy database. Patterns of occupancy of each of the occupancy zones are determined using the occupancy database. Future occupancy of the occupancy zones is predicted based on the occupancy patterns of the occupancy zones. The environmental conditions of the occupancy zones are controlled based on the detected occupancy and/or the predicted future occupancy. The environmental conditions of the occupancy zones in the occupancy space are thereby predictively controlled by simultaneously incorporating an occupant's desired settings, existing and historical temperature data within the occupancy space, and weather data. The predicted occupancy can be combined with demand response signal and energy tier rates to make optimal conditioning decisions.