FORCES

Heating, ventilation, and air conditioning (HVAC) systems in buildings are an emerging application area for model predictive control (MPC) due to the significant cost benefits that can be achieved via load shifting in modern electricity markets. In this paper, we discuss some of the opportunities and challenges associated with applying MPC to commercial HVAC systems. After defining the control problem, a decomposition of the centralized MPC is presented and demonstrated for an example system. Recent work at the Stanford University campus is also highlighted to show these ideas in practice, and an outlook for the field is given.

Calibrating subjective data biases and model predictive uncertainties in machine learning-based thermal perception predictions

A novel high-dimensional sensor calibration framework integrating thermodynamic laws in complex HVAC systems

The increasing energy demand of Heating, Ventilation, and Air Conditioning (HVAC) systems in office buildings necessitates practical, renewable-based solutions suitable for tropical climates. This study develops and evaluates a solar-assisted HVAC (SA-HVAC) configuration integrating a 6-kW photovoltaic (PV) array, 5 kWh lithium-ion battery, and rule-based supervisory controller for small smart-office applications in Abuja, Nigeria. The model was developed and simulated in EnergyPlus/OpenStudio using local weather data and dynamic occupancy schedules. Four configurations were assessed: (S0) grid-only baseline, (S1) PV-only, (S2) PV + battery, and (S3) PV + battery + solar-thermal. Results show that the hybrid PV–battery system (S2) achieved a 41% reduction in grid electricity consumption and a 31% peak-demand reduction compared to the baseline. Self-consumption (SC) and self-sufficiency (SS) improved from 0.61/0.49 to 0.84/0.67, while indoor comfort was maintained within 22–26 °C for 97–98% of occupied hours, satisfying ASHRAE 55 standards. Annual battery cycling (≈ 290 FEC) remained within safe operating limits, ensuring system longevity. The proposed SA-HVAC configuration demonstrated cost-effectiveness (≈ 4.5-year payback) and ease of implementation, requiring no advanced predictive algorithms or complex infrastructure. The findings confirm that right-sized PV and short-duration storage can significantly enhance building energy autonomy and comfort in solar-rich developing regions, providing a scalable framework for sustainable smart-building retrofits.

Abstract: Energy piles have been developed as Ground Heat Exchangers (GHEs) in Ground Source Heat Pump (GSHP) systems to enhance the thermal performance of Heating, Ventilation, and Air Conditioning (HVAC) systems for buildings. The idea of the energy pile stems from the fact that the ground temperature in depths more than 8–15m is nearly constant throughout the year. This study investigates the thermal performance of a GSHP combined with spiral-coil energy piles for space heating of a residential building in a cold climate. A three-dimensional transient Computational Fluid Dynamics (CFD) model of the spiral-coil energy pile coupled to the GSHP and the surrounding soil is developed. This model is then used to investigate the effect of design parameters, including GHE diameter and GHE pitch, on the thermal performance of the studied GSHP. Then, a five-year operation of the system is simulated. It is concluded that the increase in GHE diameter and GHE pitch enhances the thermal performance of the system. Moreover, the performance of the studied GSHP in a cold climate gradually declines in a long-term operation due to the cold accumulation in the pile and surrounding ground.

Real-time thermal comfort evaluation is not only essential in constructing the control module of Heating, Ventilation and Air Conditioning (HVAC) systems in residential buildings but also rather critical in energy conservation. In the transient thermal environment, current thermal comfort is not stable and varies from time to time. Therefore, if we only evaluate the current thermal sensation that will cause prediction error. Existing thermal comfort models mainly focus on the evaluation of current thermal comfort. However, research on the evaluation of thermal sensation variation trend is vacant. Furthermore, since individual differences play an important role in thermal comfort evaluation, physiological indices should be considered. To solve this problem, in this paper, the authors exclusively propose the concept of relative thermal sensation which accounts for the thermal sensation variation trend and give its real-time evaluation method by analysis of skin/clothes temperatures of ten local body segments using machine learning algorithms. By incorporating the relative thermal sensation model with an ordinary thermal comfort model, a novel complex thermal comfort model is derived, which has the ability to predict the current thermal comfort and the thermal sensation variation trend simultaneously and provides an early warning mechanism for thermal discomfort.

A mixing ventilation (MV) system integrated with a chilled ceiling (CC) cooling system will be a potential advanced heating, ventilation and air conditioning (HVAC) system in the modern office buildings. In this paper, an experimental study of the air distribution characteristics in a room with MV and CC was performed when considering the effect of the heat transfer of external envelope. The distributions of indoor air temperature and velocity were measured and evaluated by the vertical air temperature difference and turbulence intensity, respectively. The results showed that when chilled ceiling surface temperature ranged from 17.0 to 26.0 °C and supply air temperature was 16.0–22.0 °C, the increased trend of vertical air temperature was gradually switched to the decreased trend along the vertical direction, and the vertical air temperature differences in occupied zone were all not exceeded 0.4 °C. The average turbulence intensity was 30–41% when the internal and external sensible cooling loads equal 41.5 W/m2. The range of these evaluation indices varied clearly when the internal sensible cooling load increased from 41.5 to 69.5 W/m2, whereas they were almost the same when the external sensible cooling load increased from 41.5 to 69.5 W/m2.

Reinforcement learning (RL) for controlling building heating, ventilation, and air conditioning (HVAC) systems has shown promise in delivering energy-efficient, energy-flexible and resilient buildings. However, training RL agents in real-time in buildings is generally considered time and cost prohibitive. RL agents can be trained offline in simulated environments, reducing the time to performance once online, but complex models for simulation can be difficult to generate. What level of complexity is required in an offline training model to produce acceptable online results is unknown. This work seeks to determine how simplified models can be used to train RL agents for building HVAC control. The work uses the EnergyPlus simulation environment paired with HVAC systems modeled in Modelica to act as the real-world analogue environment. Simplified reduced-order models of the building were developed using the lumped capacitance method. The results show that an RL agent trained using Deep Q Learning on a simplified building model can be transferable to a more complex building simulation environment with comparable performance. In virtually all cases, the RL agents were able to deliver improved performance over the baseline proportional controller. Online learning gave mixed results in terms of enhancing the policy transfer process. The implementation in this paper was more suitable for improving the performance of poor policy transfer rather than fine-tuning already well-performing policies.

Protecting building occupants against the inhalation of outdoor-origin aerosols

Owing to the call for energy efficiency, the need to optimize the energy consumption of commercial buildings-responsible for over 40% of US energy consumption-has recently gained significant attention. Moreover, the ability to participate in the retail electricity markets through proactive demand-side participation has recently led to development of economic model predictive control (EMPC) for building's Heating, Ventilation, and Air Conditioning (HVAC) system. The objective of this paper is to develop a price-sensitive operational model for building's HVAC systems while considering inflexible loads and other distributed energy resources (DERs) such as photovoltaic (PV) generation and battery storage for the buildings. A Nonlinear Economic Model Predictive Controller (NL-EMPC) is presented to minimize the net cost of energy usage by building's HVAC system while satisfying the comfort-level of building's occupants. The efficiency of the proposed NL-EMPC controller is evaluated using several simulation case studies.

In recent years, the energy conservation demand attracted much attention due to the depletion of energy resource and environmental impact by increasing energy consumption. In particular, heating, ventilation and air-conditioning (HVAC) systems in buildings is responsible for significant portion of global energy demand. Heat or energy recovery is one of the key energy-efficient technologies, which reveals to overcome the increase of energy consumption in building without reducing the indoor air quality. However, in the conventional heat recovery system, only the sensible heat was recovered, but the latent heat was ignored. In this work, a novel energy recovery ventilation (ERV) model is developed with semi-permeable membrane, and the performance of sensible and latent energy subject to tropical climate conditions is investigated by both numerical and experimental methods. The 3D ERV model is comprehensively studied first by CFD simulation for analysis of important parameters, such as velocity, temperature, humidity of supply and exhaust airflow. The CFD simulation results show that the sensible and latent effectiveness could be gained at 75% and 65% respectively. Moreover, preliminary experiment is also conducted to validate the simulation results for the impacts of ERV on energy consumption. The dynamic model of HVAC is then constructed and developed in Simulink. The model predictive control strategy for the control of temperature, humidity and CO2 level will be implemented in this model for optimization of ERV control integrated into the whole HVAC system to achieve much more energy saving.

Increasing demand on heating, ventilation, and air-conditioning (HVAC) systems and their importance, as the respiratory system of buildings, in developing and spreading various microbial contaminations and diseases with their huge global energy consumption share have forced researchers, industries, and policymakers to focus on improving the sustainability of HVAC systems. Understanding and considering various parameters related to the sustainability of new and existing HVAC systems as the respiratory system of buildings are vital to providing healthy, energy-efficient, and economical options for various building types. However, the greatest opportunities for improving the sustainability of HVAC systems exist at the design stage of new facilities and the retrofitting of existing equipment. Considering the high available percentage of existing HVAC systems globally reveals the importance of their retrofitting. The attempt has been made to gather all important parameters that affect decision-making to select the optimum HVAC system development considerations among the various opportunities that are available for sustainability improvement.

Phthalates and organophosphates in settled dust and HVAC filter dust of U.S. low-income homes: Association with season, building characteristics, and childhood asthma

This paper presents the CDL-PLC translator, a tool toconvert control sequences for building heating,ventilation, and air conditioning (HVAC) systems expressedin a subset of Modelica called Control Description Language(CDL) which is undergoing standardization via the voluntaryASHRAE Standard 231P into the IEC 61131-10 XML format. Sucha translation is a step in a model-based design workflowand contributes to digitalizing the control deliveryprocess in the building sector. The translation into theIEC 61131-10 XML is the last step in the implementation ofcontrol logic developed in CDL on programmable logiccontrollers (PLCs) standardized in IEC 61131. The paperpresents the details of the translator, an example for thevalidation of a CDL block vs. the corresponding IEC block,and a practical application example for the translation ofa control sequence for a hybrid heat pump plant from a casestudy neighborhood in Belgium. Practical applications, usecases, and future developments are discussed in the contextof building industry processes.

Heating, ventilating, and air conditioning (HVAC) systems in industrial and commercial buildings constitutes 40% of the world energy consumption. Many recent studies put more emphasize on developing efficient control strategies in order to deliver acceptable occupants' comfort while maintaining optimal energy consumption. This paper introduces a state feedback control approach for heating and air conditioning systems by optimizing set points and operation modes while providing the desired indoor environmen's quality at minimum energy consumption. Simulations have been conducted using the BCVTB framework by developing the interface that allows data exchange between EnergyPlus for building's envelope and MATLAB for integrating the proposed controller. Simulation results are reported to show the potential of the state feedback control approach for saving energy while keeping suitable occupant's comfort.