System and method for aligning HVAC consumption with renewable power production with the aid of a digital computer
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Solution Overview
Problem
Current methods for estimating HVAC energy consumption are complex, invasive, and costly, often requiring detailed energy audits with specialized testing equipment, and do not effectively account for various factors influencing thermal conductivity and energy usage.
Innovation Solution
A system and method using empirically-measured values and readily-available energy consumption data to calculate HVAC energy consumption through two approaches: annual or periodic fuel requirements, and hourly or interval fuel requirements, which involve deriving building-specific parameters like thermal mass, thermal conductivity, and effective window area using short duration tests, allowing for simulation of indoor building temperature and verification of results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional energy audit methods are used to determine building thermal conductivity, then measurement precision is improved, but device complexity and loss of time increase
Solution Approach 1:
The patent creates a digital replica (digital twin) of the building's thermal characteristics by collecting and processing energy consumption data, weather data, and building characteristics data. This virtual model copies the essential thermal behavior without requiring physical testing equipment, thereby achieving measurement precision through data modeling rather than invasive physical tests.
Solution Approach 2:
The patent replaces mechanical testing equipment (blower doors, thermal cameras, infiltration meters) with an information-based system that processes existing operational data. The thermal conductivity determination shifts from a mechanical measurement process to a computational analysis of energy consumption patterns, eliminating the need for specialized testing equipment while maintaining measurement capability.
2Measurement precision
If conventional energy audit methods are used to determine building thermal conductivity, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary data collection by gathering energy consumption data, weather data, and building characteristics data that already exist from normal building operation. This preliminary action eliminates the need for time-consuming on-site testing, as the necessary information is collected passively over time rather than through intensive measurement campaigns.
Solution Approach 2:
The patent creates a digital replica of the building's thermal behavior by processing existing operational data. This virtual modeling approach copies the essential thermal characteristics without requiring physical presence or invasive testing, thereby determining thermal conductivity rapidly through computational analysis rather than prolonged field measurements.
3Loss of energy
If shell upgrades are implemented to reduce energy consumption, then energy efficiency is improved, but cost increases
Solution Approach 1:
The patent enables partial optimization by allowing users to adjust individual controllable parameters (thermostat settings, occupancy schedules, equipment operation) independently of the building shell. This partial action approach achieves energy reduction through operational adjustments rather than requiring complete shell reconstruction, thereby reducing energy loss without proportionally increasing investment cost.
Solution Approach 2:
The patent achieves energy efficiency improvements by changing operational parameters (temperature setpoints, timing schedules, equipment control settings) rather than physical parameters (insulation thickness, window U-values). This parameter change approach modifies how the building operates to reduce HVAC energy consumption without requiring costly physical upgrades to the building shell.
4Loss of energy
If smart thermostats are used to adapt HVAC operation, then energy efficiency is improved, but device complexity increases
Solution Approach 1:
The patent implements self-service functionality by enabling the thermostat to automatically learn and adapt to user behavior patterns, occupancy schedules, and environmental conditions without requiring manual programming or complex user intervention. The system autonomously optimizes HVAC operation by processing its own operational data and making adjustments, thereby reducing energy consumption while keeping the user interface simple.
Solution Approach 2:
The patent incorporates feedback mechanisms where the thermostat continuously monitors energy consumption data, weather conditions, and building response to adjust its control strategy. This feedback loop enables the system to learn from past performance and automatically optimize HVAC operation, achieving energy efficiency improvements through adaptive control rather than complex hardware modifications.
Data Source
AI summary
HVAC load can be shifted to change indoor temperature. A time series change in HVAC load data is used as input modified scenario values that represent an HVAC load shape. The HVAC load shape is selected to meet desired energy savings goals, such as reducing or flattening peak energy consumption load to reduce demand charges, moving HVAC consumption to take advantage of lower utility rates, or moving HVAC consumption to match PV production. Time series change in indoor temperature data can be calculated using only inputs of time series change in the time series HVAC load data combined with thermal mass, thermal conductivity, and HVAC efficiency. The approach is applicable for both winter and summer and can be applied when the building has an on-site renewable power system.