HVAC Energy Routine Scheduling Using Occupancy and Pricing Signals
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Solution Overview
Problem
HVAC systems lack efficient energy management solutions that balance user comfort and energy efficiency, particularly in relation to varying temperature conditions and peak energy pricing periods, leading to suboptimal energy consumption and system performance.
Innovation Solution
A data aggregation framework generates a personalized energy management schedule for HVAC systems by combining data from thermostats, temperature sensors, user presence, and weather data, using machine learning techniques to create a performance model that adjusts operation to minimize energy costs while maintaining comfort settings, and transmits instructions to control devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If HVAC system operates continuously to maintain user comfort, then user comfort is improved, but energy consumption increases
Solution Approach 1:
The system performs preliminary cooling or heating of the property before peak energy pricing periods or before predicted user arrival, storing thermal energy in building mass to reduce the need for continuous HVAC operation during high-cost periods
Solution Approach 2:
The HVAC system dynamically adjusts its operation based on real-time conditions including user presence detection, predicted arrival times, current temperature, and energy pricing signals, transitioning between different operational modes (continuous, cyclic, pre-conditioning) to optimize both comfort and energy consumption
2Reliability
If HVAC system operates at high capacity to meet peak cooling demand, then cooling performance is improved, but energy consumption and system wear increase
Solution Approach 1:
The system uses cyclic on/off operation with strategically timed intervals, leveraging the thermal mass of the building to maintain comfortable temperatures during off periods, thereby reducing peak capacity requirements and overall energy consumption while maintaining adequate cooling performance
Solution Approach 2:
The system changes operational parameters including setpoint temperature, fan speed, and compressor capacity based on real-time conditions such as user presence, outdoor temperature, and energy pricing, allowing the HVAC system to operate at optimal capacity levels rather than always at maximum
3Ease of operation
If HVAC system operates during peak energy pricing periods, then user comfort during occupied periods is improved, but energy cost increases
Solution Approach 1:
The system pre-cools or pre-heats the property before peak pricing periods begin, using lower-cost off-peak energy to establish thermal conditions that maintain comfort during peak periods without requiring high-capacity operation during expensive times
Solution Approach 2:
The system continuously monitors user presence, temperature conditions, and energy pricing signals to dynamically adjust HVAC operation, using feedback from sensors and external data sources to make real-time decisions about when and how to operate the system to minimize cost while maintaining comfort
4Device complexity
If HVAC system operates without considering user presence, then system simplicity is maintained, but energy efficiency during unoccupied periods deteriorates
Solution Approach 1:
The system automatically detects user presence using sensors and predicted arrival data, and autonomously adjusts HVAC operation without requiring manual user input or complex scheduling, making the system adaptive to occupancy patterns while maintaining reasonable complexity through automated decision-making algorithms
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for energy reduction are disclosed. In one aspect, a method includes the actions of receiving data from a thermostat, an HVAC system, and one or more temperature sensors associated with a property. The actions further include generating an HVAC performance model based on the received data from the thermostat, the HVAC system, and the one or more temperature sensors associated with the property. The actions further include receiving user data indicating user presence and user preferences. The actions further include identifying an energy penalty score. The actions further include receiving weather data. The actions further include creating an energy routine based on the HVAC performance model, the user data, the identified energy penalty score, and the received weather data. The actions further include transmitting an instruction including the energy routine to one or more devices.