Ventilation Control Strategies Research Articles

Comparing mechanical ventilation control strategies for indoor air quality: Monitoring and simulation results of a school building in northern Italy

Demand-controlled ventilation systems and carbon dioxide monitoring are critical to ensure indoor air quality (IAQ) comfort conditions. Their importance has become significantly more evident since the COVID-19 pandemic crisis. Control strategies for mechanical ventilation based on CO2 concentration are commonly used, although they are rarely applied in Italian and other Southern European country schools. Moreover, optimised ventilation systems can help maintain indoor air quality while reducing energy consumption for heating (i.e. exploiting heat recovery). The proposed paper aims to compare different remote-control strategies to manage three detached mechanical ventilation units (DMV) installed in a junior-high-school demo building in Torre Pellice (Northwest Italy), monitored with room detail since April 2021. Eight IAQ control logics are analysed considering both in situ tests, handling DMV accordingly to room sensors, and simulation tests using the Energy Management System of EnergyPlus in the neutral season. All different solutions are compared by considering air quality, temperature, and energy need indicators. Additionally, a yearly energy verification via hourly simplified analyses is performed by considering fan consumption and energy conservation. Threshold-based control logics result to be more effective than time-dependent ones, while all cases guarantee very high IAQ levels, although energy savings are limited. The choice of the correct control threshold(s) is site-dependent being influenced by user local habits.

Impact of outdoor particulate matter 2.5 pollution on natural ventilation energy saving potential in office buildings in China

Natural ventilation improves building energy performance and indoor air quality, but its potential is hindered by outdoor pollution. This paper aims to evaluate the impact of outdoor Particulate Matter 2.5 (PM2.5) pollution on natural ventilation energy saving potential in office buildings in China. Firstly, a typical office building in five representative cities across all climatic regions was designed. Secondly, related weather data were obtained and preprocessed. Thirdly, a computer model was developed under the EnergyPlus environment with smart window ventilation control, space temperature reset using adaptive thermal comfort model, and a validated pollution transportation model that calculates the indoor PM2.5 concentration. The computer model was applied to three air-conditioning systems, i.e., constant air volume (CAV), variable air volume (VAV), and variable refrigerant flow (VRF) with three evaluation indices. The results show that the natural ventilation hours rate with/without considering the outdoor PM2.5 concentration level were 15%–55% and 28%–67%, respectively. Energy savings varied across regions, with mild regions having the highest potential and cold regions having the least. CAV demonstrates the highest energy savings, followed by VAV and VRF, except in Guangzhou where VAV performs the worst. This study contributes to understanding the impact of outdoor pollution on natural ventilation potential and provides practical insights for energy-saving strategies in office buildings. Its originality lies in the evaluation methodology, comprehensive approach, sophisticated computer model, comparative analysis of air-conditioning systems, and practical implications for energy-savings. The research outcomes can guide building facility engineers in implementing ventilation control strategies while maintaining indoor thermal comfort.

Simulation-based trade-off modeling for indoor infection risk of airborne diseases, energy consumption, and thermal comfort

The transmission of airborne diseases indoors represents a significant challenge to public health. While enhancing ventilation can mitigate infection risks, it simultaneously escalates building energy consumption and alters human thermal comfort. There is limited understanding about the intricate interplay among 1) human health measured as exposure to pathogens and infection risk, 2) building energy consumption as a result of different heating, ventilation, and air conditioning (HVAC) control strategies, and 3) human thermal comfort in different climate zones. This research developed a modeling framework to evaluate the trade-offs among health, energy, and human thermal comfort and conducted simulations using school building data, considering a variety of parameters in temperature, humidity, and ventilation control. Key findings revealed that indoor temperature profoundly influences infection risk, energy consumption, and thermal comfort. Ventilation rate governs the variations of infection risks and building energy usage, while indoor relative humidity demonstrated negligible impacts. Notably, thermal comfort and low infection risk can be concurrently realized, albeit at the expense of high energy consumption. Comparing the optimal and worst environment settings in a typical U.S. climate zone, a 43% decrease in infection risks and a 61% increase in thermal comfort are observed, accompanied by an over 70% increase in energy consumption. The influences and trade-offs among infection risks, energy consumption, and thermal comfort are additionally modulated by climate characteristics.

A Review on Virtual Testbed Frameworks for Implementation of Various HVAC Control Strategies

This literature survey provides a review of virtual testbed frameworks for the implementation of various Heating, Ventilation, and Air Conditioning (HVAC) control strategies. With the advancement of technology and the increasing demand for energy-efficient HVAC systems, virtual testbeds have emerged as powerful tools for evaluating and optimizing control strategies. This survey examines the existing literature on virtual testbed frameworks, highlighting their key features, advantages, and limitations and specifically with the frameworks that supports implementation of reinforcement-based control methods and allow implementation of baseline methods for testing various control strategies. It also presents an overview of different HVAC control strategies that have been implemented and evaluated using these frameworks. The survey aims to provide researchers in the field of HVAC control with a valuable resource for understanding the state-of-the-art virtual testbed frameworks and their applications

An intelligent HVAC control strategy for supplying comfortable and energy-efficient school environment

In South Korea, school buildings require significant energy inputs for heating and air-conditioning, and the majority of the occupants are adolescent students, whose health and cognitive performance are vulnerable to poor indoor air quality (IAQ) and thermal discomfort. Using field measurements, some previous studies have reported that some Korean schools have poor IAQ and thermal conditions. Thus, it is necessary to develop effective heating, ventilation, and air-conditioning (HVAC) control strategies to improve the indoor environment and reduce energy consumption. Therefore, this study proposes an intelligent HVAC integrated control strategy that can improve indoor environmental quality (IEQ) and reduce energy consumption in school buildings. The proposed strategy utilizes an integrated neural network prediction model for IEQ and a heuristic method that can optimize control objectives (i.e., the predicted mean vote [PMV], carbon dioxide [CO2], particulate matter with diameters of 10 and 2.5 μm [PM10 and PM2.5, respectively], and HVAC energy consumption). To evaluate the control performance of the proposed strategy, the present study employs two base algorithms (i.e., a rule-based and a non-adaptive control approach) under non-disturbance and forcing disturbance scenarios. The control failure period for PMV is found to be 1.6420% and 9.4773% of the total occupancy period under the non-disturbance and forcing disturbance scenarios, respectively, while CO2 control failure does not occur under either scenario. The control failure periods for PM10 and PM2.5 were 5.1676%, and 7.1844%, respectively, under forcing disturbance. Under the non-disturbance scenario, the proposed strategy consumed 2,467.07 kWh and 870,26 kWh for heating and cooling, respectively, representing 91.1% and 84.08% of that for the rule-based algorithm. The proposed strategy can thus effectively improve the IEQ of a building and has the potential for use in the development of integrated environmental management solutions for buildings.

Sequential air pollution emission estimation using a hybrid deep learning model and health-related ventilation control in a pig building

Ammonia (NH3), carbon dioxide (CO2), and hydrogen sulfide (H2S) are predominant gases that are responsible for indoor air quality and air pollution emitted from pig buildings. They are critical for the health of pigs, farm workers, and people living nearby. To achieve an accurate estimation of gas emissions, firstly, hybrid deep learning driven sequential Concentration Transport Emission Model (DL-CTEM) was proposed to estimate the emissions of NH3, CO2, and H2S from a pig building. Then, optimal ventilation control strategies were put forward to improve health-related gas concentrations and air pollution from the pig building. Fifty-three days of hourly measurements data were divided into training data and test data for the DL-CTEM. It was shown that the mean errors between the measurements and the predictions of the proposed model for NH3, CO2, and H2S concentrations were 0.1 ppm, 79.2 ppm, and 106.3 ppb, respectively. The proposed model outperformed when it was built with an optimal structure in the long short-term memory (LSTM) layer. The mean emission rates of NH3, CO2, and H2S based on DL-CTEM were 4.2 mg min−1, 2887.5 mg min−1, and 2.1 μg min−1. It could provide a feasible way for air pollution emission estimation and health-related ventilation control in a pig building.

Deep vision-based occupancy counting: Experimental performance evaluation and implementation of ventilation control

Recently, many researchers have become interested in occupant information and occupant-centric control (OCC) strategies, aiming for efficient building operation. Combining a camera with deep learning (hereafter referred to as deep vision-based occupancy counting) is a very effective method for occupancy counting, but there are not many studies evaluating its experimental performance. Furthermore, there are insufficient studies on implementing control using deep vision. The purpose of this study was to experimentally evaluate the performance of deep vision-based occupancy counting and to implement deep vision-based OCC in reality. First, we evaluated the performance of deep vision-based occupancy counting for six offices. Second, we implemented a deep vision-based energy recovery ventilator control strategy in a small office and compared the indoor air quality and energy consumption with those from traditional control strategies. As a result, deep vision-based occupancy counting showed significantly higher performance (root mean square error (RMSE): 0.883, normalized RMSE (NRMSE): 0.141). The larger the floor area, the more frequently the prediction of the number of occupants was lower than the actual number. The control results showed that deep vision-based ventilation control could properly maintain the indoor CO2 concentration with 24–35% lower ventilation rates compared to traditional ventilation control strategies. Furthermore, the proposed strategy was effective in reducing the electrical energy consumption of energy recovery ventilator and heat pump.

Study of Natural Ventilation and Solar Control Strategies to Improve Energy Efficiency and Environmental Quality in Glazed Heated Swimming Pools in a Dry Mediterranean Climate

This paper studies the energy behavior of several public heated swimming pools with semi-transparent covers located in southeastern Spain with high consumption of their air-conditioning installations. The scientific novelty of the work is to determine the influence of solar radiation on the energy performance of this type of building and to demonstrate that the use of passive systems such as natural ventilation and solar control enhance the energy efficiency in glazed heated swimming pools in a warm semi-arid climate. The methodology used consisted of on-site measurements of current hygrometric behaviour and a study of alternative solutions by simulation of virtual models with improved hygrothermal conditions. In the on-site measurements, thermographic images were used to analyse the thermal envelope and hot-wire probe measurements to determine the temperature distribution and air velocity inside the pool enclosure. For the study of alternative solutions, simulations were carried out, including an analysis of incident solar radiation and different natural ventilation and solar control solutions. The results obtained showed that the current hygrothermal behaviour of the interior spaces does not comply with the regulations on the thermal quality of the indoor environment. The results show that the proposed natural ventilation and solar control solutions substantially improved the hygrothermal properties and energy savings of the pools analysed. This work offers an alternative solution that avoids the implementation of costly air conditioning systems and the energy consumption of installations, promoting more sustainable renovations that contribute to improving the indoor comfort of users with interventions that are compatible with existing buildings.

Impact of mechanical ventilation control strategies based on non-steady-state and steady-state Wells-Riley models on airborne transmission and building energy consumption.

Ventilation is an effective solution for improving indoor air quality and reducing airborne transmission. Buildings need sufficient ventilation to maintain a low infection risk but also need to avoid an excessive ventilation rate, which may lead to high energy consumption. The Wells-Riley (WR) model is widely used to predict infection risk and control the ventilation rate. However, few studies compared the non-steady-state (NSS) and steady-state (SS) WR models that are used for ventilation control. To fill in this research gap, this study investigates the effects of the mechanical ventilation control strategies based on NSS/SS WR models on the required ventilation rates to prevent airborne transmission and related energy consumption. The modified NSS/SS WR models were proposed by considering many parameters that were ignored before, such as the initial quantum concentration. Based on the NSS/SS WR models, two new ventilation control strategies were proposed. A real building in Canada is used as the case study. The results indicate that under a high initial quantum concentration (e.g., 0.3 q/m3) and no protective measures, SS WR control underestimates the required ventilation rate. The ventilation energy consumption of NSS control is up to 2.5 times as high as that of the SS control.

Estimation of Unmeasured Room Temperature, Relative Humidity, and CO2 Concentrations for a Smart Building Using Machine Learning and Exploratory Data Analysis

Smart buildings that utilize innovative technologies such as artificial intelligence (AI), the internet of things (IoT), and cloud computing to improve comfort and reduce energy waste are gaining popularity. Smart buildings comprise a range of sensors to measure real-time indoor environment variables essential for the heating, ventilation, and air conditioning (HVAC) system control strategies. For accuracy and smooth operation, current HVAC system control strategies require multiple sensors to capture the indoor environment variables. However, using too many sensors creates an extensive network that is costly and complex to maintain. Our proposed research solves the mentioned problem by implementing a machine-learning algorithm to estimate unmeasured variables utilizing a limited number of sensors. Using a six-month data set collected from a three-story smart building in Japan, several extreme gradient boosting (XGBoost) models were designed and trained to estimate unmeasured room temperature, relative humidity, and CO2 concentrations. Our models accurately estimated temperature, humidity, and CO2 concentration under various case studies with an average root mean squared error (RMSE) of 0.3 degrees, 2.6%, and 26.25 ppm, respectively. Obtained results show an accurate estimation of indoor environment measurements that is applicable for optimal HVAC system control in smart buildings with a reduced number of required sensors.

Investigating dilution ventilation control strategies in a modern U.S. school bus in the context of the COVID-19 pandemic

Fresh air ventilation has been identified as a widely accepted engineering control effective at diluting air contaminants in enclosed environments. The goal of this study was to evaluate the effects of selected ventilation measures on air change rates in school buses. Air changes per hour (ACH) of outside air were measured using a well-established carbon dioxide (CO2) tracer gas decay method. Ventilation was assessed while stationary and while traversing standardized route during late autumn/winter months in Colorado. Seven CO2 sensors located at the driver’s seat and at passenger seats in the front, middle, and rear of the bus yielded similar and consistent measurements. Buses exhibited little air exchange in the absence of ventilation (ACH = 0.13 when stationary; ACH = 1.85 when mobile). Operating the windshield defroster to introduce fresh outside air increased ACH by approximately 0.5–1 ACH during mobile and stationary phases. During the mobile phase (average speed of 23 miles per hour (mph)), the combination of the defroster and two open ceiling hatches (with a powered fan on the rear hatch) yielded an ACH of approximately 9.3 ACH. A mobile phase ACH of 12.4 was achieved by the combination of the defroster, ceiling hatches, and six passenger windows open 2 inches in the middle area of the bus. A maximum mobile phase ACH of 22.1 was observed by using the defroster, open ceiling hatches, driver window open 4 inches, and every other passenger window open 2 inches. For reference, ACHs recommended in patient care settings where patients are being treated for airborne infectious diseases range from 6 to ≥12 ACHs. The results indicate that practical ventilation protocols on school buses can achieve air change rates thought to be capable of reducing airborne viral transmission to the bus driver and student passengers during the COVID-19 pandemic.

Field investigation of pollutant characteristics and targeted ventilation control strategies in high-ceiling aircraft spraying workshop

To achieve efficient ventilation and purification in the high-ceiling painting workshop is faced with the contradiction between effect and energy consumption. Understanding the characteristics of gaseous pollutants is of paramount importance for ventilation and purification system design. The composition and concentration of volatile organic compounds (VOCs) emitted from aircraft painting workshop were sampled by Tenax-TA tubes and analyzed with gas chromatography-mass spectrometry (GC-MS). Approximately 50 types of VOCs (detection rate>50%) were detected in the aircraft spraying workshop, with percentages of 36.3% for esters, 31.9% for aldehyde ketones, 28.5% for biphenylenes, 1.9% for alcohols and 1.4% for alkanes. The TVOC concentrations in the workshop were 48.6 mg/m3 and 132.4 mg/m3, during the varnish painting and the finish painting processes, respectively, and these are several times higher than those observed in other industries (automobile painting and wooden furniture painting). The field test data of spraying workshops from 197 spraying factories in five industry sectors were collected from field measurement and existing literature. Some VOCs components are the general pollutants in the painting workshops, such as Acetic acid, butyl ester, Toluene, and 2-Butanone. A novel ventilation model using multiple target purification units is proposed to eliminate the pollutants in an aircraft spraying workshop. Compared with the original trench exhaust system, the air volume of the proposed targeted ventilation system is reduced by 75%, and the energy consumption is reduced by 45,000 kW·h per aircraft.

Comparative Numerical Energy Analysis of Decentralized Ventilation Adapting to Local Norway Climates

The present work evaluates the performance of different decentralized ventilation control strategies in the local Norway climate. Evaluation is performed using a monthly time-step energy analysis and the IDA-ICE simulation tool for a comparative primary energy analysis on the strategy combinations. Primary energy comparison is conducted with respect to a centralized constant air volume system for comparative analysis. The evaluation results show that the representative decentralized ventilation (DV) system has the most significant energy performance. The lower heat recovery efficiency significantly impacts on the ventilation energy of DV system in the cold climate, and the low specific fan power can efficiently be used for zone cooling in summer.

Quantitative Assessment of Indoor CO2 Concentration of a Comprehensive Office Building

To better understand the extent of different ventilation strategies, a set of multi-zone models of a comprehensive office building in Harbin were conducted based on CONTAMW. The main purpose aims to assess various factors affecting the concentration of indoor CO2 in real situations by building network models, in order to seek appropriate ventilation control strategies to improve indoor air quality. Firstly, under the combined action of stack effect and wind pressure, the model was verified by field measurement. Secondly, CO2 concentration peak value, attenuation rate and air exchange rate were analysed in conference rooms on different floors under typical seasons with the doors and windows closed. Moreover, different doors and windows opening schedules of conference rooms were set during the meeting to reduce CO2 concentration. Results demonstrate that CO2 concentration in conference rooms are affected by many factors including different climatic parameters, height of building, building envelope leaking characteristics, occupant participated ventilation behaviour etc. and multi-zone simulation analysis shows the necessity of its application at comprehensive building. The study highlights the need for effective guiding significance of ventilation control to reduce the concentration of indoor CO2 in areas where there are more people indoors in short period of time, which is also meaningful for the personnel health and building design.

Using Co-simulation between EnergyPlus and CONTAM to evaluate recirculation-based, demand-controlled ventilation strategies in an office building

A coupled energy, airflow, and contaminant transport building model was developed using co-simulation between EnergyPlus and CONTAM. The model was used to analyze different strategies to control supply air delivery and return air recirculation rates including the use of demand-controlled ventilation (DCV) strategies. Strategies were evaluated for their effects on indoor pollutant concentrations and energy use of an office building in Trondheim, Norway. Typically, office buildings in Norway employ 100% outdoor air ventilation systems. Measurements in the office building served as the basis to develop the coupled model. The same building was also simulated with the outdoor conditions of Beijing.The results showed that all the simulated DCV strategies yielded reductions in energy use compared to a baseline, schedule-based strategy. Using recirculation of return air was also an energy efficient measure which increased the otherwise low indoor humidity levels in Trondheim. Using CO2-based DCV may result in increased levels of indoor particulate (PM2.5) from outdoors but using PM2.5 monitoring in the ventilation control strategies reduced indoor concentration of PM2.5 and energy usage. However, the low outdoor PM2.5 levels in Trondheim may not justify its use in this location. The Beijing case revealed that the indoor levels of PM2.5 can be reduced below the World Health Organization requirement of annual average of 10 μg/m3 using PM2.5 control.Co-simulation results revealed that it is possible to both reduce energy use and improve IAQ by controlling the outdoor air fraction based on multiple pollutants while also considering local outdoor environments.

Design and implementation of an occupant-centered self-learning controller for decentralized residential ventilation systems

Mechanical ventilation systems acquired relevance in the past decades as a solution to guarantee air exchange in residential energy retrofit. The individualization of the control strategies to the user preferences could enhance the acceptance of innovative technologies and meet the energy efficiency targets for buildings. In this publication, a novel occupant-centered self-learning control strategy for decentralized ventilation systems is introduced. The controller has a standard-based default control profile, which learns the occupants’ preferences using a classification algorithm. The proposed controller is first simulated and compared to other available mechanical ventilation control strategies. A stochastic model for the manual operation of ventilation systems based on artificial comfort profiles is implemented in the simulation model. Results show that the proposed controller improves the indoor environmental conditions (measured with the relative humidity and CO2 concentration) without disregarding energy efficiency. The self-learning controller can successfully adapt itself to these profiles. The proposed solution was implemented in a real apartment. Measurements were carried out for three months, where two occupants operated the ventilation devices. The installed ventilation concept succeeded in maintaining an acceptable indoor environment, while the distinctive user profiles were learned in each room. The bedrooms were shifted towards lower air exchange rates, while in the rest of the rooms higher air exchange rates were preferred. This implementation confirms the individualization potential of this controller. Finally, the novel self-learning strategy provides an adaptive solution for residential decentralized ventilation controllers, targeting the occupant preferences regarding comfort, health and energy efficiency.

A hybrid deep transfer learning strategy for thermal comfort prediction in buildings

Since the thermal condition of living spaces affects the occupants' productivity and their quality of life, it is important to design effective heating, ventilation and air conditioning (HVAC) control strategies for better energy efficiency and thermal comfort. An essential step in HVAC control and energy optimization is thermal comfort modeling. Recently, data-driven thermal comfort models have been preferred over the Fanger's Predicted Mean Vote (PMV) model due to higher accuracy and ease of use. However, the unavailability of comprehensive labelled thermal comfort data from the occupants poses a significant modeling challenge. This paper addresses data inadequacy issues by adopting ‘transfer learning’ to leverage well learned knowledge from source domain (same climate zones) to target domain (different climate zone) where modeling data is sparse. Specifically, a Transfer Learning based Convolutional Neural Networks-Long Short Term Memory neural networks (TL CNN-LSTM) is designed for effective thermal comfort modeling that exploits the spatio-temporal relations in the thermal comfort data. The significant modeling parameters for TL CNN-LSTM are identified using the Chi-squared test. Further, the lack of sufficient samples across all thermal conditions in the available thermal comfort datasets was handled by Synthetic Minority Oversampling Technique (SMOTE). Experiments with two source (ASHRAE RP-884 and Scales Project) and one target (Medium US office) datasets demonstrate the ability of TL CNN-LSTM in achieving an accuracy of >55% with limited data in target buildings. The limitation of TL CNN-LSTM is its continued dependence on intrusive parameters and the challenges in assessing its adaptability to different climate zones.

An integrated approach for the analysis and modeling of road tunnel ventilation. Part I: Continuous measurement of the longitudinal airflow profile

The knowledge of the flow field inside road tunnels under normal operation, let alone fire conditions, is only approximate and partial. The reason is that while the full three-dimensional, unsteady problem is out of reach of numerical methods, on the other hand accurate measurement of the airflow in road and railway tunnels constitutes an extremely demanding task. The present work, structured as a twofold study, takes up the challenge and proposes an original integrated experimental and numerical approach for the analysis and modeling of flow inside a road tunnel and its ventilation systems, aiming at defining a methodology for the creation of “digital twins” of the system itself, on which advanced ventilation and smoke control strategies can be tested and fine-tuned. In this first part, an innovative experimental facility for the continuous acquisition of the longitudinal velocity profile along the whole length of a road tunnel has been designed and built. The facility consists of a survey rake with five bidirectional vane anemometers, which is mounted on a small electric vehicle that can travel through the tunnel at constant speed. This paper reports the design procedure of the measurement facility, with particular focus on the conception and realization of the vehicle carrying the survey rake. Results of the first experimental campaign carried out under the 11611 meters long Mont Blanc road tunnel are presented to corroborate the validity of the approach adopted and the accuracy of the measurement chain.

Evaluation of performance of energy efficient hybrid ventilation system and analysis of occupants’ behavior to control windows

Hybrid ventilation systems have become increasingly attractive, because they are considered to provide good indoor air quality (IAQ) with low energy consumption. In this study, we evaluate the performance of a hybrid ventilation system (operating on both mechanical and natural ventilation mode) by monitoring the IAQ (e.g. temperature, humidity, CO2 levels) and the energy consumption of a multifamily building. We then investigate occupants' window-opening behavior during the heating season by means of window sensors. The results show that the hybrid ventilation system maintains a good level of IAQ while being energy efficient. We also find that occupants’ window-opening behavior is significantly affected by indoor temperature and CO2 concentration. We therefore recommend adopting them as input parameters when designing ventilation control strategies to enhance the performance.

Building energy management decision-making in the real world: A comparative study of HVAC cooling strategies

In recent years, various heating, ventilation, and air-conditioning (HVAC) control strategies have been proposed to reduce buildings’ energy consumption and environmental impacts. Due to different research designs, it is impossible to directly compare the reported performance of these strategies and determine the best approach for operating real-world buildings. To fill this gap, this research uses a well-validated building simulation model to holistically evaluate the performance of 12 existing HVAC cooling strategies in reducing the total cooling energy use, peak demand, and cooling energy cost. The findings include: i) The ON/OFF and night setup control strategies could lead to even higher energy cost than the 24/7 operation baseline; ii) demand-limiting control methods performed well across the three energy performance metrics and achieved the highest energy reduction ratios ranging from 9.8% to 10.5%; iii) precooling and extended precooling reduced the peak load most significantly by 15.4% and 21.4%, respectively; and iv) the resistive-capacitive (RC) network-based precooling optimization resulted in the highest electricity cost savings with reduction ratios of 16.6% and 12.9%, based on the two common price schedules. Besides providing valuable insights to the research community, this study offers a practical method to help building operators analyze and select the best HVAC control strategies for their energy management goals.