Predictive HVAC Scheduling With Thermal Storage for Peak Pricing
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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 operation and increased energy costs.
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 user comfort settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If HVAC systems operate continuously to maintain user comfort settings, then user comfort is improved, but energy consumption increases
Solution Approach 1:
The system performs preliminary actions by pre-cooling or pre-heating spaces before peak pricing periods or before predicted occupancy. The energy management schedule proactively adjusts temperatures in anticipation of future conditions, allowing the HVAC system to be curtailed during expensive periods while maintaining comfort when needed. This resolves the contradiction by shifting energy consumption to off-peak times rather than continuous operation.
Solution Approach 2:
The system dynamically adjusts HVAC operation based on real-time conditions including weather forecasts, energy pricing signals, and predicted occupancy. Rather than static continuous operation, the system adapts its setpoints and scheduling continuously, optimizing the balance between comfort and energy consumption by responding to changing conditions.
2Use of energy by moving object
If HVAC systems are curtailed during peak pricing periods to reduce energy costs, then energy consumption is reduced, but user comfort deteriorates
Solution Approach 1:
The system performs preliminary cooling or heating before peak pricing periods to store thermal energy in the building's thermal mass. This allows the HVAC system to be curtailed during expensive periods while the stored thermal energy maintains acceptable temperatures. User comfort is preserved through advance preparation rather than continuous operation.
Solution Approach 2:
The system continuously monitors actual temperature conditions, occupancy patterns, and user preferences, adjusting the energy management schedule in real-time. If comfort thresholds are approached during curtailment periods, the system responds by adjusting operation, ensuring comfort requirements are met while minimizing energy consumption during peak periods.
3Temperature
If HVAC systems operate at full capacity to meet cooling or heating demands, then temperature control is improved, but service life reduces
Solution Approach 1:
The system dynamically modulates HVAC operation to avoid sustained full-capacity operation. By using predictive scheduling and thermal storage, the system operates at lower capacities more frequently, reducing mechanical stress and wear on components while maintaining temperature control through advance preparation and real-time adjustments.
Solution Approach 2:
The system uses periodic cycling with extended off-periods rather than continuous full-capacity operation. The predictive energy management schedule creates rhythmic patterns of operation that allow equipment to rest and recover, reducing cumulative wear while meeting temperature requirements through strategic timing of operation cycles.
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.