arXiv subjects

Arash J. Khabbazi

Publications and source records attributed to Arash J. Khabbazi.

4 recordsLinked to original sources

arXiv2026

Scalable Supervisory HVAC Control for Linear Objectives

Advanced control of heating, ventilation, and air-conditioning (HVAC) systems can substantially reduce energy costs and pollution. However, real-world adoption of popular algorithms among researchers, such as model predictive control and reinforcement learning, remains limited due in part to their high deployment and commissioning costs. Here, we develop two nearly commissioning-free supervisory controllers tailored to objectives that depend linearly on the controlled thermal load, such as energy costs and pollution. The controllers require, at most, two easily-estimable thermal parameters, forecasts of energy prices and occupant temperature preferences over a prediction horizon, and an indoor temperature measurement. In residential cooling simulations, both controllers perform essentially as well under traditional time-invariant electricity pricing as an omniscient optimal controller with exact model information and perfect forecasts, and attain up to 86.6% of the omniscient cost savings under increasingly prevalent time-varying pricing. These results suggest that simple, structure-exploiting controllers may capture most of the attainable value of advanced supervisory HVAC control with linear objectives, while avoiding the data, modeling, tuning, and computational burdens that hinder real-world deployment.

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arXiv2026

Indirect data-driven predictive control and the state-space predictor

We define trajectory predictive control (TPC) as a class of indirect data-driven predictive control (DDPC) methods that represent future outputs as linear in past inputs/outputs and future inputs. TPC unifies many DDPC variants with different predictor structures. We introduce a predictor with a state-space representation and show that with it, TPC inherits the mature theory of linear model predictive control. In numerical experiments, the state-space predictor outperforms existing predictors, especially for small training datasets.

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arXiv2026

Small HVAC Control Demonstrations in Larger Buildings Often Overestimate Savings

How much energy, money, and emissions can advanced control of heating and cooling equipment save in real buildings? To address this question, researchers sometimes control a small number of thermal zones within a larger multi-zone building, then report savings for the controlled zones only. That approach can overestimate savings by neglecting heat transfer between controlled zones and adjacent zones. This paper mathematically characterizes the overestimation error when the dynamics are linear and the objectives are linear in the thermal load, as usually holds when optimizing energy efficiency, energy costs, or emissions. Overestimation errors can be large even in seemingly innocuous situations. For example, when controlling only interior zones that have no direct thermal contact with the outdoors, all perceived savings are fictitious. This paper provides an alternative estimation method based on the controlled and adjacent zones' temperature measurements. The new method does not require estimating how much energy the building would have used under baseline operations, so it removes the additional measurement and verification challenge of accurate baseline estimation.

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arXiv2025

Lessons learned from field demonstrations of model predictive control and reinforcement learning for residential and commercial HVAC: A review

A large body of simulation research suggests that model predictive control (MPC) and reinforcement learning (RL) for heating, ventilation, and air-conditioning (HVAC) in residential and commercial buildings could reduce energy costs, pollutant emissions, and strain on power grids. Despite this potential, neither MPC nor RL has seen widespread industry adoption. Field demonstrations could accelerate MPC and RL adoption by providing real-world data that support the business case for deployment. Here we review 24 papers that document field demonstrations of MPC and RL in residential buildings and 80 in commercial buildings. After presenting demographic information -- such as experiment scopes, locations, and durations -- this paper analyzes experiment protocols and their influence on performance estimates. We find that 71% of the reviewed field demonstrations use experiment protocols that may lead to unreliable performance estimates. Over the remaining 29% that we view as reliable, the weighted-average cost savings, weighted by experiment duration, are 16% in residential buildings and 13% in commercial buildings. While these savings are potentially attractive, making the business case for MPC and RL also requires characterizing the costs of deployment, operation, and maintenance. Only 13 of the 104 reviewed papers report these costs or discuss related challenges. Based on these observations, we recommend directions for future field research, including: Improving experiment protocols; reporting deployment, operation, and maintenance costs; designing algorithms and instrumentation to reduce these costs; controlling HVAC equipment alongside other distributed energy resources; and pursuing emerging objectives such as peak shaving, arbitraging wholesale energy prices, and providing power grid reliability services.

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