Quantum Annealing for Real-Time HVAC Model Predictive Control

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Solution Overview

Problem

Existing building HVAC systems face challenges in achieving real-time, energy-efficient control due to complex non-linear dynamics, large-scale discrete variables, and computational inefficiencies in model predictive control (MPC), making it difficult to optimize energy usage while maintaining indoor comfort.

Innovation Solution

Employing quantum annealing for non-linear model predictive control (MPC) of HVAC systems by formulating optimization as quadratic unconstrained binary optimization (QUBO) problems and using a quantum processing unit (QPU) to solve these complex optimization problems in real-time.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of energy

If model predictive control (MPC) is used to optimize HVAC systems, then energy efficiency is improved, but real-time computational response becomes difficult to achieve

Engineering Contradiction:

ImproveHVAC energy consumptionVSAvoidcomputational response speed

Core Design Contradiction:

Loss of energyVSSpeed

Solution Approach 1:

The patent replaces the classical computational system with a quantum computing system to solve the optimization problem. The quantum processor performs quantum annealing to find optimal HVAC control solutions, substituting the mechanical/computational system with a quantum-based system that achieves faster computation speeds while maintaining energy optimization capabilities.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Speed

If quantum annealing is used for optimization, then computational speed is improved, but handling non-linear models with discrete variables becomes challenging

Engineering Contradiction:

Improveoptimization computation speedVSAvoidcomplexity of non-linear discrete optimization

Core Design Contradiction:

SpeedVSDevice complexity

Solution Approach 1:

The patent transforms the non-linear optimization problem with discrete variables into a quadratic unconstrained binary optimization (QUBO) problem. This parameter transformation allows the complex non-linear problem to be reformulated in a way that quantum annealing can efficiently solve, changing the mathematical representation from non-linear discrete variables to quadratic binary variables.

Inventive Principle:

Principle #35Parameter changes

3Speed

If simplified non-linear models are used for MPC, then real-time control is achieved, but inaccuracy increases

Engineering Contradiction:

Improvereal-time control capabilityVSAvoidcontrol accuracy

Core Design Contradiction:

SpeedVSMeasurement precision

Solution Approach 1:

The patent replaces the simplified model approach with a quantum computing system that can handle complex non-linear models without approximation. The quantum processor's ability to perform parallel quantum computations allows it to solve accurate non-linear models in real-time, eliminating the need to trade off model complexity for computational speed.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

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

Achieves significant energy savings, reducing total electric usage by 80% and electric bills by 21% with improved computational speed, compared to traditional methods.

Implementation Method 1

a quantum processing unit (QPU) is programmed to perform a non-linear model predictive control strategy in real-time... wherein the non-linear model predictive control strategy uses a quantum annealer to minimize an amount of energy

Methodology Applied

Scientific EffectQuantum annealing:

Data Source

PatentUS20260009552A1Quantum computing for real-time building HVAC controls

Publication Date: 2026.01.08 SYRACUSE UNIVERSITY

AI summary

An optimization solution based on quantum annealing for model predictive control (MPC) of a rooftop unit (RTU) for minimizing energy usage. The solution achieved less than 2 percent differences from conventional approaches and improved computational speed from hours to seconds. The solution also demonstrated an 80% reduction in total electric usage and a 21 percent electric bill reduction considering day-ahead price time-of-use demand response signals.