Energy Consumption Of HVAC System Research Articles

A Comparison of Programmed Controlled Existing System vs. Electric Resistive Heating for a University Building in Newfoundland

Buildings consume in excess of 30% of the total energy worldwide. In the Canadian context, commercial and institutional buildings contribute to around 14% of the overall energy usage, and space heating emerges as the predominant end-use category, constituting approximately 57% of this consumption. This underscores a considerable potential for energy savings in the realm of building energy consumption. This paper compares the energy consumption for space heating at the Core Science Facility (CSF) of the Memorial University of Newfoundland (MUN), Canada. The analysis contrasts the current system, utilizing hot water from fuel oil-fired boilers, with a proposed system suggesting the replacement of the oil-fired boiler with an electric resistive boiler, by employing a building energy model (BEM) created with the OpenStudio application. The findings indicate that beyond the anticipated enhancements in energy efficiency, a supplementary energy saving of approximately 7% is attainable through the proposed transition. Comparing the simulation outcomes with actual data reveals that the projected consumption from the Building Energy Model (BEM) is lower than the actual figures. This difference is attributed to the model’s development, which involved distinct considerations and assumptions compared to the actual conditions such as construction materials, building occupancy, infiltration and exfiltration, interconnected buildings, energy usage by equipment and lighting, HVAC system energy consumption, and transmission losses through piping which can significantly influence the building’s energy consumption.

Sex-based thermal comfort zones and energy savings in spaces with joint operation of air conditioner and fan

Fan and air conditioner combined systems (FACS) have been demonstrated as a viable alternative to conventional air conditioning systems, providing thermal comfort to occupants while consuming less energy. However, few studies have quantitatively established occupant comfort zones for FACS operation and analyzed its energy saving effects. An experiment was conducted first to develop thermal comfort zone in spaces with FACS operation. As the indoor temperature rises, the upper limit of air speed accepted by occupants increases. Females exhibit an upper comfort range parameter of 29.8 °C and 1.4 m/s, while males demonstrate 29.3 °C and 1.5 m/s. There are noticeable differences in comfort ranges between males and females, especially in cooler conditions. Based on the established comfort zone, energy simulation studies were conducted for most regions of China to investigate the potential energy savings resulting from the implementation of FACS in ASHRAE standard office building. Buildings incorporating FACS demonstrate a reduction in HVAC system energy consumption by more than 16 % when compared to indoor temperature set at 26 °C. In hot summer and warm winter climates, HVAC system energy savings of over 100 GJ/year can be achieved using FACS for the standard building. Considering sex differences in FACS operation leads to additional energy savings.

Multi-objective optimization of pressure regulators in buildings’ HVAC systems

The control accuracy of the adjustable dynamic flow balance valve regulators is closely related to the energy consumption of the buildings' HVAC system. Therefore, optimizing the control accuracy of the pressure regulator plays an important role in reducing the energy consumption of the HVAC system and indoor cooling and heating comfort. In the study, the transient dynamics calculation method was used to simulate the movement process of the regulator diaphragm. The rebound force and fatigue life of the diaphragm are the optimization goals. An improved whale algorithm is proposed to optimize the Kriging model. The sensitivity analysis of the objective function surrogate model was carried out by the single factor analysis method. Based on the improved seagull algorithm, the multi-objective optimization of the diaphragm was carried out, and the corresponding MATLAB program was compiled. The optimized Kriging proxy model has higher fitting accuracy and smaller prediction error. The rebound force of the optimized diaphragm is reduced by 13.13% compared with that before optimization, and the fatigue life is increased by 87.89%. The dynamic characteristics and control accuracy of the adjustable dynamic flow balance valve after optimizing the diaphragm are verified by experiments. The maximum flow value is reduced by 2.03%, the transition time is reduced by 15.1%, and the stable flow value is closer to 20 t/h. This study contributes to the development of machine learning models with higher fitting accuracy and provides a new feasible design method for reducing energy consumption in buildings’ HVAC systems.

Predictive control optimization of chiller plants based on deep reinforcement learning

The energy consumption of HVAC systems is enormous, with chiller plants accounting for more than 50% of it. To improve energy efficiency, chiller systems are typically optimized at fixed intervals based on real-time building cooling loads, which usually assumes that the cooling load remains constant within the control interval. However, in many real applications, the current optimal control may be changed when the cooling load suddenly fluctuates. Although this issue can be addressed by shortening the control intervals, more frequent control can damage the equipment or increase energy consumption. To tackle this problem, this paper proposes a model-free predictive control method based on Reinforcement Learning (RL) control and Long Short-Term Memory (LSTM) prediction networks. The LSTM network aims to predict the future cooling load based on historical cooling load data, while the RL is used to make the best control for all chiller plants. By this way, the chilled water supply temperature setpoints can be optimized with the consideration of both instantaneous and predicted cooling loads to minimize energy consumption. In order to validate the effectiveness of the proposed method, an experimental simulation model was constructed based on actual chiller system parameters. Experimental results show that the energy-saving performance of the proposed method is superior to rule-based control (9% improvement), closely comparable to the model predictive control (0.73% difference), and further enhances energy-saving effects compared to non-predictive RL control without shortening the control interval. Additionally, the proposed method just learns by continuously interacting with the environment and does not require any accurate equipment models, making it a viable alternative for buildings when lacking extensive sensors and device information.

Occupant’s thermal responses to asymmetric radiant thermal environment with warm wall and cool ceiling, Part A: Overall thermal responses

A radiant ceiling cooling (CC) system integrated with a mechanical ventilation system, which can improve human thermal comfort and reduce HVAC system energy consumption simultaneously, has been extensively applied in the modern office building with glass curtain walls. In a ceiling cooling room with a glass curtain wall, the horizontal and vertical asymmetric radiation caused by the warm wall and cool ceiling may result in different thermal responses of human body. Therefore in this paper, occupant’s thermal responses in a simulated ceiling cooling office room with mixing ventilation (MV) or underfloor air distribution (UFAD) were investigated by a combination of subjective and objective experiments. Indoor operative temperature was increased from 23.0 °C to 30.0 °C as the surface temperatures of warm wall and cool ceiling were from 30.0 °C to 40.0 °C and from 14.0 °C to 27.0 °C. The results showed that the indoor thermal environment with CC + MV or CC + UFAD was comfortable and acceptable as the indoor operative temperature was from 25.0 °C to 27.0 °C. The thermal neutral operative temperature in the room was 25.6 °C with CC + MV and 26.4 °C with CC + UFAD, and the corresponding thermal neutral skin temperatures was 33.4 °C and 33.7 °C, respectively. Moreover, the thermal comfort stability of indoor operative temperature was close to 0.30 °C−1 with CC + MV or CC + UFAD, and the corresponding thermal comfort stability of mean skin temperature was 0.90 °C−1–0.94 °C−1. There were clearly linear relationships between thermal sensation or thermal preference and indoor operative temperature or mean skin temperature, and these relationships may be beneficial for the intelligent control of a radiant ceiling cooling system combined with a mechanical ventilation system.

Meta-learning of personalized thermal comfort model and fast identification of the best personalized thermal environmental conditions

The model of personalized thermal comfort can be learned via various machine learning algorithms and used to improve the individuals’ thermal comfort levels with potentially less energy consumption of HVAC systems. However, the learning of such a model typically requires a substantial number of thermal votes from the considered occupant, and the environmental conditions needed for collecting some votes may be undesired by the occupant in order to obtain a model with good generalization ability. In this paper, we propose to use a meta-learning algorithm to reduce the required number of personalized thermal votes so that a personalized thermal comfort model can be obtained with only a small number of feedback. With the learned meta-model, we derive a method based on the backpropagation of neural networks to quickly identify the best environmental and personal conditions for a specific occupant. The proposed identification algorithm has an additional advantage that the thermal comfort, indicated by the mean thermal sensation value, improves incrementally during the data collection process. We use the ASHRAE global thermal comfort database II to verify that the meta-learning algorithm can achieve an improved prediction accuracy after using 5 thermal sensation votes from an occupant to make adaptations. In addition, we show the effectiveness of the fast identification algorithm for the best personalized thermal environmental conditions with a thermal sensation generation model built from the PMV model.

Deep reinforcement learning optimal control strategy for temperature setpoint real-time reset in multi-zone building HVAC system

Determining a proper trade-off between energy consumption and indoor thermal comfort is important for HVAC system control. Deep Q-learning (DQN) based multi-objective optimal control strategy is designed for temperature setpoint real-time reset to balance the energy consumption and indoor air temperature. In addition, this study develops an EnergyPlus-Python co-simulation testbed to evaluate DQN control strategy in a simulation environment. A case study experiment is conducted to evaluate the performance of DQN control strategy for real-time reset of supply air temperature and chilled supply water temperature setpoint in a multi-zone building VAV system. The developed EnergyPlus-Python co-simulation testbed is used to train and test the DQN control strategy for performance analysis. The applied DQN strategy leans to update control actions (i.e. temperature setpoint) through interaction with the simulation environment. Simulation results show that the DQN control strategy is effective in finding a proper trade-off between the energy consumption of HVAC system and indoor air temperature. Meanwhile, the DQN control strategy can find a proper temperature setpoint reset sequence in smaller training episodes, and the control actions can be stable after ten DQN training episodes. This study provides a preliminary direction of deep reinforcement learning control strategy for temperature setpoint real-time reset in multi-zone building HVAC systems.

Prediction of Hourly Air-Conditioning Energy Consumption in Office Buildings Based on Gaussian Process Regression

Accurate prediction of air-conditioning energy consumption in buildings is of great help in reducing building energy consumption. Nowadays, most research efforts on predictive models are based on large samples, while short-term prediction with one-month or less-than-one-month training sets receives less attention due to data uncertainty and unavailability for application in practice. This paper takes a government office building in Ningbo as a case study. The hourly HVAC system energy consumption is obtained through the Ningbo Building Energy Consumption Monitoring Platform, and the meteorological data are obtained from the meteorological station of Ningbo city. This study utilizes a Gaussian process regression with the help of a 12 × 12 grid search and prediction processing to predict short-term hourly building HVAC system energy consumption by using meteorological variables and short-term building HVAC energy consumption data. The accuracy R2 of the optimal Gaussian process regression model obtained is 0.9917 and 0.9863, and the CV-RMSE is 0.1035 and 0.1278, respectively, for model testing and short-term HVAC system energy consumption prediction. For short-term HVAC system energy consumption, the NMBE is 0.0575, which is more accurate than the standard of ASHRAE, indicating that it can be applied in practical energy predictions.

Experimental study on the synthetic dust loading characteristics of air filters

• The loading characteristics of bag-type air filters for ASHRAE and A2 fine test dust were studied through experiments. • A pressure drop empirical equation model was proposed via linear regression. • The dust loading mass is introduced in the evaluation of the air filter pressure drop. The energy consumption of filtration is an important part of the energy consumption of HVAC systems, so reasonable evaluation of the average pressure drop during the entire life cycle of an air filter is very important. However, the loading characteristics of bag-type air filters for ASHRAE test dust and A2 fine test dust have not been well studied. Medium-efficiency bag-type air filters widely used in HVAC systems were selected as the research object, and dust loading experiments at different concentrations of ASHRAE test dust and A2 fine test dust were conducted in order to reveal the evolution of air filter pressure drop under different air flow rate and dust loading state conditions. Based on experimental data and via linear regression, an empirical equation model of pressure drop calculation with air flow rate and dust loading mass as independent variables is proposed, which can be used to predict the pressure drop of air filters. The results show that the absolute value of relative error between the pressure drop values predicted by the empirical equation model and experimental results is basically within 7%, and that the model is accurate and reliable. The applicability of the model is verified through the experimental data from references. Based on this model, it is possible to predict the pressure drop of air filters in HVAC systems under constant or variable air flow rates and different dust loading masses, and to reasonably evaluate the average pressure drop throughout the life cycle of the filter.

Air Quality and Comfort Characterisation within an Electric Vehicle Cabin in Heating and Cooling Operations.

This work is aimed at the experimental characterisation of air quality and thermal profile within an electric vehicle cabin, measuring at the same time the HVAC system energy consumption. Pollutant concentrations in the vehicle cabin are measured by means of a low-cost system of sensors. The effects of the HVAC system configuration, such as fresh-air and recirculation mode, on cabin air quality, are discussed. It is shown that the PM concentrations observed in recirculation mode are lower than those in fresh-air mode, while VOC concentrations are generally higher in recirculation than in fresh-air mode. The energy consumption is compared in different configurations of the HVAC system. The novelty of this work is the combined measurement of important comfort parameters such as air temperature distribution and air quality within the vehicle, together with the real time energy consumption of the HVAC system. A wider concept of comfort is enabled, based on the use of low-cost sensors in the automotive field.

Non-dimensional analysis for heat pipe characteristics in the heat pipe heat exchanger as energy recovery device in the HVAC systems

Heat pipe heat exchangers (HPHE) were widely applied as a recovery energy device in HVAC systems and significantly can reduce the energy consumption of HVAC systems. This study performs experimental and non-dimensional analysis using the Buckingham Pi theorem to develop a correlation of heat pipe performance as an energy recovery device in air HVAC systems. The experiment was conducted to investigate heat transfer characteristics in the heat pipe heat exchangers. The fresh-air temperature in the evaporator section (Te,in) was varied at 30–45 °C, fresh air velocity varied at 1.5–2.5 m/s, and the number of rows HPHE varied at 3, 6, and 9 rows. While the return air temperature was entering the condenser's inlet side, HPHE (Tc,in) was kept at 23 °C. The procedures of the Buckingham Pi theorem were adopted to determine non-dimensional parameters and found a correlation between Pi forms. Each non-dimensional parameters value was calculated based on the measured data. The correlation between non-dimensional parameters can be solved simultaneously using the multiple linear regression with the logarithmic transformations method. The new correlation has been found in the form Sp Number. The results show that Sp numbers is affected primarily by the number of rows (n), effectiveness (ε), and Reynold numbers (Re,D). The equation Sp number can be used to predict the thermal resistance of a single heat pipe and is very useful to know the performance of a heat pipe in the design and operating stages.

Field studies on thermal comfort of passengers in airport terminals and high-speed railway stations in summer

In transportation buildings such as airport terminals and railway stations, the thermal comfort of passengers is associated with the energy consumption of HVAC systems. Previous studies did not reach consistent conclusions on the differences in thermal comfort demands of passengers with different dwell times in waiting halls. In addition, the thermal comfort of passengers in various sessions during the departure and arrival processes, such as security checks and walking, was not addressed. Therefore, we conducted field studies in the waiting halls of ten transportation buildings and 12 simulated experiments of departure and arrival processes in five of these terminals, with 10 subjects in each experiment. A total of 1,576 valid questionnaires were collected from the waiting halls. The results showed that passengers were more adaptable to the indoor thermal environment than the results foreseen by the PMV–PPD model. As the dwell time increased, the adaptability of the passengers to the indoor thermal environment was approximately the same. However, the neutral standard effective temperature (SET) and preferred SET increased with time. The current indoor thermal design parameters of GB 50736 can meet passengers’ thermal comfort during short-distance departure and arrival processes. Under low air velocity conditions (< 0.2 m/s), the 90% acceptable indoor operative temperature (Top) ranged from 21.9 °C to 25.4 °C for the security check and from 23.9 °C to 25.2 °C for the long-distance processes.

A Strategy to Maximally Utilize Outdoor Air for Indoor Thermal Environment

In order to reduce the energy consumption of HVAC systems in buildings, the use of energy-saving solutions is necessary. One of these solutions is ventilation, which is usually used for maintaining acceptable indoor air quality and thermal comfort. As the change in outdoor environment is unpredictable and the occupant control is spontaneous, it is critical to control the windows and HVAC systems to achieve a maximum use of outdoor air for indoor ventilation. A new rule-based control strategy that could change the opening factor of windows is proposed in this study and its effectiveness was tested in five representative climates, ranging from a subtropical region to a severely cold region. A building model was set up and the indoor air temperature and energy consumption were predicted using EnergyPlus. The results show that the proposed control strategy can utilize ventilation to maintain a comfortable indoor environment with an annual uncomfortable percentage in an occupied period lower than 5%, thus leading to an energy-saving rate of 13.5–55.6%. The simulation results indicate that there are periods of ventilation available during the summer in climate zones with hot summers and warm winters, whereas the control strategy has a better energy-saving performance in temperate areas. This study conducted a preliminary exploration for practical applications of the combined operation of controllable natural ventilation and HVAC systems in buildings.

Study on influencing factors for occupant window-opening behavior: Case study of an office building in Xi'an during the transition season

Window-opening behavior profoundly affects indoor air quality, thermal comfort, and the energy consumption of HVAC systems in office buildings. This paper presents the results of a field study on manual window control conducted in five typical individual offices in the building of a research institute in the city of Xi'an, located in the cold region of China. The average duration of window-open and window-closed states was 5.3 h/day and 6.7 h/day during working hours, respectively, and thus the window-open state accounted for 44% of the total duration during the transition season. Unlike previous studies, the results show that once a window was opened when occupants first come to the office, it would not be left like that for long, until the occupants left the office at the end of day. In addition, window opening behavior is more strongly correlated with indoor PM2.5 concentration and indoor temperature. A new algorithm has been developed that applies dummy variables based on a binary logistic to provide a more accurate description of the relationship between the window state and influencing factors at different levels. The proposed model increased the prediction accuracy of the window being open by 5.9% compared to the continuous variable model. This study focuses on understanding the characteristics of window operation and establishing a more accurate model that offers suitable data and theoretical support for applications to simulate energy consumption in office building.

Application Research of Intelligent Control Algorithm in Air Conditioning System

In order to reduce the energy consumption of HVAC system, we analyzed and compared modern intelligent control algorithm applied to HVAC control system, introduced the basic principles of fuzzy control, neural network and genetic algorithm, and analyzed the applicability of intelligent algorithm in HVAC system with engineering examples.

Fault diagnosis model of refrigeration system combining Boruta and LightGBM methods for energy saving

Energy consumption of HVAC system accounts for a large proportion of building energy consumption, and it is necessary to improve the energy efficiency of the system to achieve the purpose of energy saving. Once the HVAC system malfunctions, the energy efficiency of the system will decrease, resulting in unnecessary energy waste. Aiming at common failures of refrigeration systems, this research proposes a fault diagnosis model combining Boruta and LightGBM methods to diagnose faults in time. The results show that compared with other three tree-based models, this optimized LightGBM model can not only efficiently diagnose the refrigerant charge amount faults of the VRF system, achieving a fault diagnostic accuracy of 97.52%, but also can be transplanted to other systems for fault diagnosis, thus effectively avoiding unnecessary energy waste caused by those failures.

Energy Conservation Potential of Economizer Controls Using Optimal Outdoor Air Fraction Based on Field Study

In commercial buildings, HVAC systems are becoming a primary driver of energy consumption, which already account for 45% of the total building energy consumption. In the previous literature, researchers have studied several energy conservation measures to reduce HVAC system energy consumption. One of the effective ways is an economizer in air-handling units. Therefore, this study quantified the impact of the outdoor air fraction by economizer control type in cooling system loads based on actual air-handling unit operation data in a hospital. The optimal outdoor air fraction and energy performance for economizer control types were calculated and analyzed. The result showed that economizer controls using optimal outdoor air fraction were up to 45% more efficient in cooling loads than existing HVAC operations in the hospital. The energy savings potential was 6–14% of the differential dry-bulb temperature control, 17–27% of the differential enthalpy control, 8–17% of the differential dry-bulb temperature and high-limit differential enthalpy control, and 16–27% of the differential enthalpy and high-limit differential dry-bulb temperature control compared to the no economizer control. The result of this study will contribute to providing a better understanding of economizer controls in the hospital when the building operates in hot-humid climate regions.

Implementation of Chiller Plant Management System to Optimize the Building Primary HVAC System Energy Consumption

Implementation of Chiller Plant Management System to Optimize the Building Primary HVAC System Energy Consumption

Effects of increasing urban albedo in the Greater Toronto Area

Increasing surface reflectivity decreases the skin and air temperature, which potentially reduces cooling energy demands. The state of the art online numerical Weather Research and Forecasting model (WRF) is used to investigate the effect of increasing albedo in Toronto, Ontario, during the 2018 heat wave period (July 2nd through July 5th) on urban climate and building energy consumption. The study couples the WRF with a multi-layer of the Urban Canopy Model (ML-UCM) and Building Energy Model (BEM). The ML-UCM is a part of the land-surface parameterization to predict the heat and moisture fluxes from canopies to atmosphere. The BEM is coupled with Building Effect Parameterization to predict the energy consumption of buildings. BEM simulates the effect of heat generation from buildings on urban climate. The reflectivity of roofs, walls and roads are increased from 0.2 to 0.65, 0.60 and 0.45, respectively. Albedo enhancement leads to a decrease in air temperature by around 1°C and an increase in wind speed which induce a reduction in skin temperature. The combined effect of decreased solar heat gain by buildings and decreased air temperature reduced the energy consumption of HVAC systems by 3-5%, confirming the positive effect of increasing the albedo on urban climate.

Analysis of the field tests efficiency of indoor environmental control and energy saving technology: the cases of Solar Decathlon China 2018

The International Solar Decathlon initiated by the US Department of Energy in 2002 has been conducted for 17 years, but the literature on the use of field test big data to study the performance of design projects during the competition is scarce. This paper summarizes energy saving technologies used in past Solar Decathlons in the literature. The indoor environment test data of 5 demo participating teams of Solar Decathlon China (SDC) 2018 are organized and analyzed. The energy consumption and indoor environment data of buildings of SDC 2018 are comprehensively compared with those of past solar competition buildings. The energy consumption characteristics and indoor thermal environment characteristics of this zero-energy building are summarized. An energy evaluation index for unit energy consumption, namely energy consumption per square meter per day, is proposed. The unit energy consumption of buildings during the solar competition is 0.28–0.51 kWh/(d⋅m2), with an average of 0.35 kWh/(d m2). HVAC system energy consumption exceeds 50%. The unit energy consumption of the air conditioning system ranges between 0.13–0.23 kWh/(d⋅m2) with an average of 0.18 kWh/(d⋅m2). This can be used as a reference for future solar energy system capacity designs. According to the results of the analysis of the competition data, the recommendations for the construction of zero-energy houses for intermittent use are given in the design of the architecture design and the HVAC system. Furthermore, a control strategy to ensure indoor environment comfort and system energy saving has also been proposed.