HVAC Load Shaping Using Building Thermal Mass Constraints
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Solution Overview
Problem
Current methods for estimating building heating and cooling energy consumption are complex, invasive, and costly, often requiring detailed energy audits and specialized testing equipment, which are time-consuming and prone to inaccuracies, while also failing to provide practical models for determining actual and potential energy consumption effectively.
Innovation Solution
A system and method using empirically-measured values and readily-available energy consumption data to calculate building heating and cooling energy requirements, involving two approaches: one for annual or periodic fuel consumption and another for hourly or interval fuel consumption, which derive building-specific parameters like thermal mass, thermal conductivity, and effective window area through 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 worsen due to invasive testing requirements
Solution Approach 1:
The patent creates a digital replica (digital twin) of the building's thermal system using simplified measurements of easily observable parameters. Instead of directly measuring complex thermal conductivity through invasive tests, the system copies the building's thermal behavior by measuring outdoor temperature, indoor temperature, and energy consumption, then uses these copies to infer thermal properties through mathematical modeling.
Solution Approach 2:
The patent replaces mechanical/invasive physical testing systems with a computational model-based approach. Instead of using blower door tests, thermal cameras, and physical inspections to measure thermal conductivity, the system substitutes these with mathematical equations that calculate thermal properties from readily available operational data (temperatures and energy consumption records).
2Measurement precision
If detailed energy audits are performed to accurately determine energy consumption, then measurement precision is improved, but loss of time worsens due to the time-consuming nature of comprehensive audits
Solution Approach 1:
The system enables the building's energy system to self-report its characteristics by automatically collecting and analyzing its own operational data (energy consumption, indoor temperatures) along with outdoor weather data. The building essentially measures itself through its normal operation without requiring external auditors to perform invasive tests, thereby eliminating time loss while maintaining precision.
Solution Approach 2:
The patent creates a virtual model that copies the building's energy consumption patterns by analyzing readily available utility bills and operational data. This digital copy allows for precise energy consumption analysis without requiring physical presence or time-consuming on-site testing, as the system processes copied data from existing sources.
3Measurement precision
If invasive testing equipment is used to measure thermal properties, then measurement precision is improved, but ease of operation worsens due to the complexity of specialized testing procedures
Solution Approach 1:
The patent replaces complex mechanical testing procedures with computational analysis. Instead of requiring operators to perform blower door tests, set up thermal cameras, or conduct controlled experiments with specialized equipment, the system substitutes these with automated mathematical calculations using easily collected data (temperatures and energy bills), dramatically improving ease of operation while maintaining measurement precision.
Solution Approach 2:
The patent introduces mathematical modeling and computational algorithms as intermediaries between simple measurements and complex thermal property determination. Rather than directly measuring difficult-to-obtain thermal properties through complex procedures, the system uses intermediate calculations that transform easily measured parameters (outdoor temperature, indoor temperature, energy consumption) into accurate thermal conductivity values.
4Loss of energy
If comprehensive building shell upgrades are implemented to reduce energy consumption, then energy savings are improved, but loss of money worsens due to the high cost of shell improvements
Solution Approach 1:
The patent enables dynamic optimization of HVAC operation by using the digital twin model to simulate and evaluate different thermostat settings and operational strategies in real-time. Instead of static shell upgrades, the system dynamically adjusts heating and cooling schedules to minimize energy consumption based on predicted thermal behavior, providing cost-effective savings without physical modifications.
Solution Approach 2:
The system achieves energy savings by changing operational parameters (thermostat temperatures, timing, and setpoints) rather than physical building parameters (insulation, windows, shell). The digital twin model allows optimization of these operational parameters to reduce energy consumption by leveraging the building's thermal mass and characteristics, providing savings equivalent to shell upgrades but through low-cost operational changes.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach simplifies the estimation of energy consumption, reduces the need for invasive testing, and provides accurate fuel consumption data that can be used for economic analysis, enabling cost-effective energy savings without requiring detailed audits, and is applicable for both heating and cooling seasons.
Implementation Method 1
Time series change in indoor temperature for the time period is iteratively constructed as a function of the time series change in HVAC load and the thermal mass, thermal conductivity, and HVAC efficiency for the building
Implementation Method 2
Time series change in indoor temperature for the time period is iteratively constructed as a function of the time series change in HVAC load and the thermal mass, thermal conductivity, and HVAC efficiency for the building
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 PV system.