System and method for reducing peak energy consumption load of a renewable-resource-power-production- system-connected building with the aid of a digital computer
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Solution Overview
Problem
Current methods for reducing peak energy consumption in buildings are inefficient and require costly upgrades or invasive energy audits, lacking practical models for determining actual and potential energy consumption for heating and cooling.
Innovation Solution
A system and method using a digital computer to calculate energy consumption through empirically-measured values and utility data, deriving building-specific parameters like thermal mass and thermal conductivity, and shifting HVAC loads to reduce peak demand charges, utilizing on-site renewable resource power production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional energy audits and invasive testing are performed to determine building thermal conductivity, then measurement precision is improved, but device complexity and loss of time increase
Solution Approach 1:
The patent replaces physical invasive testing equipment (blower doors, thermal cameras, measurement tools) with a computational system that uses existing utility billing data and simple weather data to calculate thermal conductivity. The system substitutes mechanical measurement processes with mathematical modeling and data analysis, eliminating the need for physical intrusion into the building while maintaining measurement capability.
Solution Approach 2:
The system creates a virtual model of the building's thermal performance by copying and analyzing existing operational data (utility bills, weather data) rather than performing physical measurements. This digital twin approach allows thermal conductivity determination without physical testing, saving time and avoiding disruption to building occupants.
2Use of energy by moving object
If building shell upgrades are implemented to reduce thermal conductivity, then energy consumption is reduced, but manufacturing precision and loss of time increase
Solution Approach 1:
The system changes the approach from physical parameter modification (building shell upgrades) to operational parameter optimization (HVAC control strategies). By adjusting thermostat setpoints, scheduling, and system operation based on the calculated thermal conductivity, the system achieves energy reduction without physical construction work, avoiding the complexity and time associated with building renovations.
Solution Approach 2:
The system enables the building to self-optimize its energy consumption by using the calculated thermal conductivity to automatically adjust HVAC operations. The building effectively serves itself by leveraging its own operational data and weather data to determine optimal control strategies, eliminating the need for external construction interventions.
3Use of energy by moving object
If manual thermostat adjustments are made to reduce HVAC usage, then energy consumption is reduced, but manufacturing precision worsens
Solution Approach 1:
The system implements a feedback loop where utility billing data and weather data are continuously analyzed to calculate thermal conductivity, which then informs optimized HVAC control strategies. This closed-loop approach ensures that energy reduction actions are based on accurate building-specific parameters rather than manual estimates, maintaining optimization precision while reducing energy consumption.
Solution Approach 2:
The system performs preliminary calculation of thermal conductivity using historical utility and weather data before implementing HVAC control optimizations. This advance preparation ensures that subsequent energy reduction actions are precisely tailored to the building's actual thermal characteristics, avoiding the trial-and-error nature of manual adjustments.
4Loss of energy
If peak demand charges are reduced through load shifting, then loss of energy is reduced, but device complexity increases
Solution Approach 1:
The system implements dynamic HVAC control that adjusts operations in real-time based on calculated thermal conductivity, outdoor temperature, and forecasted demand charges. Rather than static pre-programmed schedules, the system continuously adapts its control strategy to current and predicted conditions, achieving peak load reduction through flexible, responsive operation rather than complex infrastructure 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 peak energy consumption load. 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.