Researchers have developed a new control method that improves energy performance and reduces emissions in smart buildings equipped with renewable energy systems and battery storage.
Study: Decarbonization of Building Operations with Adaptive Quantum Computing-Based Model Predictive Control. Image Credit: klyaksun/Shutterstock.com
The study, published in Engineering, outlines an adaptive model predictive control (MPC) framework that integrates quantum computing techniques to optimize building operations. By leveraging the Quantum Approximate Optimization Algorithm (QAOA) alongside a learning-based parameter tuning approach, the method addresses the challenges of real-time energy management in complex building systems.
Background
Energy optimization in buildings is a growing area of interest due to rising energy costs, regulatory pressures, and the demand for lower carbon footprints. Common strategies include metaheuristics like evolutionary algorithms and deterministic techniques such as MPC, which is valued for its ability to incorporate future forecasts and constraints when managing HVAC systems, energy flexibility, and on-site energy generation.
However, MPC's effectiveness can be limited by its computational intensity, particularly for nonlinear, large-scale systems commonly found in commercial, institutional, or high-performance buildings. As the complexity of building systems grows, so does the time required to compute optimal control decisions, making real-time responsiveness a challenge.
Quantum computing offers a promising direction for addressing these limitations, enabling the solution of large-scale optimization problems with significantly improved computational efficiency. This research explores how a quantum-based MPC formulation could be implemented to improve operational performance in real building energy systems.
Methods
The proposed method was developed for smart buildings equipped with key technologies: photovoltaic (PV) solar panels and a battery energy storage system. These technologies are increasingly standard in energy-efficient building projects seeking LEED, WELL, or other certifications.
The researchers reformulated the MPC problem for building energy optimization as a quadratic unconstrained binary optimization (QUBO) problem. This made it solvable using the Quantum Approximate Optimization Algorithm (QAOA), a hybrid quantum-classical algorithm suited for near-term quantum devices.
A learning-based parameter transfer scheme was introduced to enhance the QAOA’s performance. This scheme uses Bayesian optimization to predict optimal initial circuit parameters, reducing the number of iterations required and improving computational efficiency.
To evaluate the method, simulations were carried out using real-world energy data from two buildings on Cornell University’s Ithaca campus: Carpenter Hall and Baker Laboratory. These buildings differ in their energy profiles and system configurations, making them suitable test cases. Historical data from 2021 informed the initial parameter learning, while data from 2022 was used for empirical evaluation. The simulations covered four distinct months—January, April, July, and October—to account for seasonal variability.
Results and Discussion
Simulation results for January demonstrated clear variability in net load demand, with peaks occurring at different times of day. Energy consumption closely followed these patterns but deviated due to the integration of PV generation and battery storage. The adaptive QAOA-based MPC strategy enabled more flexible energy control, allowing the building to draw from storage or reduce demand in response to forecasted conditions.
Initial phases showed slightly higher net energy consumption, attributed to the exploratory nature of the Bayesian optimization process. However, as the control system adapted, the net consumption decreased, often falling below the raw load demand due to more effective use of stored and renewable energy.
The proposed quantum-based strategy delivered a 6.8 % improvement in energy efficiency compared to deterministic MPC. It also outperformed quantum annealing-based methods in terms of computational speed. Most notably, the approach yielded a 41.2 % reduction in annual carbon emissions, highlighting its potential value in sustainable building operations.
The research also evaluated constraint violations. The certainty equivalence-based MPC (CEMPC), which assumes fixed expectations under uncertainty, showed consistently higher energy consumption and more constraint violations. In contrast, the adaptive QAOA-based approach operated much closer to the theoretical lower bounds, indicating better handling of system uncertainty.
Implications for the Built Environment
The findings suggest strong potential for integrating quantum-enhanced MPC into building energy systems—particularly in facilities already incorporating solar generation, energy storage, and advanced HVAC control. For architects, engineers, and facility managers, this approach offers a way to optimize existing infrastructure without significantly increasing control system complexity.
As governments and industry stakeholders push for net-zero and low-carbon buildings, scalable solutions that reduce computational demands while improving real-time adaptability are essential. The ability to manage building systems more dynamically could support demand-side management, reduce peak loads, and improve resilience in grid-interactive buildings.
Journal Reference
Ajagekar, A. & You, F. (2025). Decarbonization of Building Operations with Adaptive Quantum Computing-Based Model Predictive Control. Engineering. DOI: 10.1016/j.eng.2025.02.002, https://www.sciencedirect.com/science/article/pii/S209580992500061X
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