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This paper focuses on how the existing sealed office typology can adapt to a warming world while minimizing the high energy demand for space cooling, through Dynamic Cooling , by adjusting the cooling temperature profile towards the average human circadian rhythm to mitigate heat stress during times of heightened thermal sensitivity. This concept is explored through a study in which we compared the effects of Dynamic Cooling , constant cooling, and no cooling on the core body temperature of 21 participants as well as a thermal dynamic simulation of 270 office space configurations under the same cooling profiles, comparing the total energy demand, peak loads, and overall hours of discomfort. The results of the study suggest the different conditioning profiles have no direct impact on the overall change in core body temperature throughout the day (difference < 0.04 K) but do influence the overall range throughout (from 0.37 K under constant cooling to 0.53 K under Dynamic Cooling). Additionally, there is no clear correlation between elevated indoor temperatures and CBT during the morning hours, questioning the necessity and effectiveness of pre-lunch cooling. This is underlined by the simulation findings, which reveal a non-linear relationship between savings in cooling demand and a decline in thermal comfort, with a 51.3 % reduction in ideal cooling energy demand and a 25.2 %–36 % increase of Over-Temperature-Kelvin-Hours, from a steady state cooling profile to a profile of Dynamic Cooling. The results of the study and simulation challenge traditional cooling standards and highlight the need for a balance between minimizing thermal discomfort and the necessary cooling energy demand. [ABSTRACT FROM AUTHOR]

[Display omitted] • Thermostatic air-conditioning control can discomfort occupants by overcooling spaces. • Machine vision + infrared thermography measures face, nose, and hand temperatures. • Human skin temperature distribution predicts cool/neutral/warm thermal sensation. • Infrared biometric sensor-controller targets neutral-to-slightly warm sensation. • Office-building trial demonstrated comfort improvement and 42% cooling-load reduction. To improve occupant comfort and save energy in buildings, we have developed a closed-loop air conditioning (AC) sensor-controller that predicts occupant thermal sensation from the thermographic measurement of skin temperature distribution, then uses this information to reduce overcooling (cooling-energy overuse that discomforts occupants) by regulating AC output. Taking measures to protect privacy, it combines thermal-infrared (TIR) and color (visible spectrum) cameras with machine vision to measure the skin-surface temperature profile. Since the human thermoregulation system uses skin blood flow to maintain thermoneutrality, the distribution of skin temperature can be used to predict warm, neutral, and cool thermal states. We conducted a series of human-subject thermal-sensation trials in cold-to-hot environments, measuring skin temperatures and recording thermal sensation votes. We then trained random-forest classification machine-learning models (classifiers) to estimate thermal sensation from skin temperatures or skin-temperature differences. The estimated thermal sensation was input to a proportional integral (PI) control algorithm for the AC, targeting a sensation level between neutral and warm. Our sensor-controller includes a sensor assembly, server software, and client software. The server software orients the cameras and transmits images to the client software, which in turn assesses occupant skin temperature distribution, estimates occupant thermal sensation, and controls AC operation. A demonstration conducted in a conference room in an office building near Houston, TX showed that our system reduced overcooling, decreasing AC load by 42% when the room was occupied while improving occupant comfort (fraction of "comfortable" votes) by 15 percentage points. [ABSTRACT FROM AUTHOR]

• Coupling PCMs with an air conditioner condenser for its performance is studied experimentally. • Rising PCM quantity from 7 to 11 kg, reduces air conditioner consumption by 7.8 % and 10.4 %, respectively. • Integrating 11 kg PCM yields 0.5 °C room temperature reduction compared with air conditioners without PCM. • The higher system performance is achieved for 4 h of system working during the day time. • Enhancing the PCM's lower thermal conductivity using nanoparticles for more system performance could be studied. Air conditioning systems are vital for modern comfort but consume a substantial portion of global energy, contributing to environmental concerns and rising energy costs. PCMs can store and release energy during phase transitions, making them promise to reduce cooling loads. This study endeavors to empirically investigate the influence of integrating hybrid Phase Change Materials (PCMs), specifically PCMSP24E and PCMSP26E, with a real vapor compression refrigeration system. The primary focus is on assessing the consequential effects on the energy consumption of the refrigeration system and the interior environmental conditions within a building, including temperature and relative humidity. The energy usage of the split air conditioner wherein the outdoor unit is attached to PCMs tube heat exchanger for precooling the incoming air is investigated. Experiments reveal that PCM precooling effectively reduces the ambient air temperature entering the air conditioning condenser, leading to substantial energy savings. Increasing the PCM quantity enhances the system performance and refrigeration unit power saving. With 11 kg of PCMs, energy consumption by the air conditioning system decreases by 10.4 %, and indoor air temperature and relative humidity reduce by 0.5° and 5 %, respectively. Hence, PCM integration ensures a more consistent indoor temperature profile, enhancing occupant comfort and reducing temperature fluctuations. [ABSTRACT FROM AUTHOR]

To facilitate the integration of buildings to the power grid, battery-equivalent models have been proposed to characterize and quantify aggregate flexibility from building loads. An analytical method has been proposed to construct such a flexibility model for thermostatically controlled loads. Since this analytical method is derived from a simplified representation of building thermal dynamics, it is necessary to evaluate the resulting flexibility model in a more realistic context. This paper focuses on the validation of this analytical method for estimating aggregate flexibility from residential air conditioning (ACs) systems. A high-fidelity model is first developed to mimic thermal behaviors of a residential AC system through a co-simulation between Modelica and EnergyPlus. A population of virtual residential AC systems is then generated by randomizing parameters of the high-fidelity model. Finally, an aggregate flexibility model is constructed for the residential AC population using the analytical method and validated using results from simulation of the high-fidelity model. It is found that the flexibility estimated using the analytical method is with reasonable accuracy, but can be improved by explicitly incorporating heat gains into the method. [ABSTRACT FROM AUTHOR]

Conceptually, many energy poverty studies to date have been narrowly focused on inadequate indoor heating, paying little attention to other domestic energy services. Yet there are indications that a growing number of households in Europe are struggling to achieve adequate levels of indoor cooling, with adverse consequences for their health, well-being and productivity. This situation is exacerbated by changing global weather patterns, with many countries facing increases in the frequency and intensity of extreme heatwaves. There is limited understanding of the ways in which households respond to extreme heat, and consequently how this might create greater demand for indoor space cooling and air conditioning, and the consequences for increased stress on power grids and conflicts with carbon reduction goals. Using custom-built survey data collected from 2337 households in Gdańsk (Poland), Prague (Czech Republic), Budapest (Hungary) and Skopje (North Macedonia), along with in-depth qualitative fieldwork with 55 households in the same cities, this paper presents novel evidence on the issue of summertime energy poverty and space cooling difficulties. We identify the driving forces of household vulnerability to excessive indoor heat, in terms of risk of exposure, adaptive capacity, and sensitivity, and explore the implications for addressing energy poverty. [ABSTRACT FROM AUTHOR]

• Investigation of novel hollow porous ceramic cuboids-heat pipes as heat and mass exchanger for evaporative cooling systems. • Formulation of mathematical model for heat and mass transfer. • Testing a proof-of-concept laboratory model. The work presented in this paper investigates design, computer modelling and testing of a sub-wet bulb temperature evaporative cooling system for space air conditioning in buildings. The target application of evaporative cooling technology is particularly for geographical regions with hot and dry climate. A laboratory prototype made of integrated finned heat pipes and a water filled hollow porous ceramic cuboids was been built and tested. The design exploits the high thermal conductivity of heat pipes as effective heat transfer device and the good wettability characteristic of porous ceramic material. Key thermal performance metrics of the prototype were modelled and validated experimentally under controlled laboratory conditions of temperature (30, 35 and 40 °C) and relative humidity (35–55%). Under typical dry climates conditions (ambient air dry bulb temperature of 35°C and relative humidity of 35%), the measured supply air dry bulb temperature was 22.3 °C, which is lower that the ambient air wet bulb temperature, and maximum cooling capacity of approximately 196 W per unit surface area (m2) of wet porous ceramic cuboid. Furthermore, it was shown that the measured prototype cooler wet bulb effectiveness was 1.05 while the dew point effectiveness was 0.73. [ABSTRACT FROM AUTHOR]

The American Society for Heating, Refrigeration, and Air Conditioning Engineers (ASHARE) prescribes fixed ventilation rates for the operation of heating, ventilation, and air conditioning (HVAC) systems, which is sub-optimal in terms of controlling indoor air pollutants and energy consumption. This paper relies on a physics-based model of a residential house to predict the concentrations of indoor pollutants such as ozone, formaldehyde (HCHO), and particulate matter (PM) as function of time, outdoor conditions, and indoor emissions. The model captures real-world scenarios such as pollutants entering the house from ambient air through an air handling unit (AHU) and pollutants emitted in the house. A dynamic optimization problem is then formulated and solved to calculate the optimal ventilation rate at each time instant, that minimizes the total energy consumption, while maintaining the indoor pollutant concentrations below or as close as possible to their respective acceptable thresholds. For the considered example day, operation under optimized conditions reduces the peak pollutant concentration by 31%, time of exposure to undesirable concentrations by 48%, and energy consumption of the AHU by 17.7%. [ABSTRACT FROM AUTHOR]

Abstract A stepped short tube orifice (SSTO) consisting of two serial-connected short tube orifices with different diameters was recently proposed as an inexpensive expansion device in air conditioning/heat pump system. In this study, a split air conditioner/heat pump employing stepped short tube orifices was investigated experimentally to further evaluate the performance of the SSTO air conditioner/heat pump system. At first, the mass flow rate of refrigerant (R-22) through stepped short tube orifices and an original expansion device were studied in both cooling and heating flow directions to verify the throttling capacity difference of these two type of expansion devices. Then, the influence of geometric sizes of SSTOs, refrigerant charge amount and environment temperature on the performance of the SSTO air conditioner/heat pump were examined. Besides, the performance of the SSTO systems also compared with that of the original system with capillary tube under nominal and varying conditions. The results show that 18# SSTO system performed the best COP among all the five SSTO systems and one original system in nominal cooling and heating conditions, though the heating capacity of the 18# SSTO system was slightly inferior to that of the original system due to the insufficient throttling capacity of 18# SSTO in heating mode. [ABSTRACT FROM AUTHOR]

A bstract We assessed whether increasing airflow with an electric fan is similarly effective as decreasing air temperature with air cooling (AC) in preventing heat-related reductions in productivity, and elevations in body temperatures and discomfort in a warm/humid indoor environment. In 48 experimental trials, we compared the reduction in the human heat stress response of sixteen participants during 135 min of intermittent arm ergometry at a fixed heart rate of 110 beats min−1, from a simulated tropical environment (HOT; 30 °C, 70%RH; wind < 0.2 m s−1) to that observed with either a, (i) 7 °C reduction in air temperature (AC; 23°C, 70%RH, wind < 0.2 m s−1); or (ii) facilitated airflow (FAN; 30°C, 70%RH, wind = 4.2 m s−1). Cumulative work was similarly improved (+11%) by FAN compared to AC. Likewise, reductions in rectal temperature, thermal sensation, and thermal discomfort were similar with the two different cooling strategies. Sweat losses in the FAN trial were higher compared to AC but lower than HOT without fanning. In conclusion, fanning offers an effective method for alleviating thermal stress and preventing productivity losses for workers exposed to environmental heat. Moving air instead of chilling it may require a little more sweating, but it can save electricity and hence lower greenhouse gas emissions compared to AC. [ABSTRACT FROM AUTHOR]

Highlights • The prospects of different working fluids and their combinations in an ORC driven air conditioning system are investigated. • Ambient conditions considering the outdoor temperatures of 30°C, 35°C and 40°C with 50% relative humidity is selected. • Working fluids R245fa, R134a, R600a, and R600, and their combinations are the investigated. • Sensitivity analysis of different parameters on the second law efficiency and UA values of heat exchangers is evaluated. • Multi-objective optimization of the exergetic efficiency and the UA values of heat exchangers is carried out. Abstract Present work aims to investigate the thermal performance assessment of different combinations of working fluids for an Organic Rankine Cycle powered Vapor Compression Cycle (ORC-VCC) for air-conditioning applications. Analysis of system performance is conducted for a fixed cooling load of a small conditioned space having a sensible heat factor of 0.7, at different ambient conditions. A preliminary evaluation of the system at the baseline conditions was conducted considering the second law efficiency, overall heat capacity of exchangers, overall internal exergy destruction, and overall Coefficient of Performance. Afterwards, parametric study was performed for the best performing candidates from the preliminary analysis using second law efficiency and overall heat capacity of heat exchangers. The system parameters considered for the study include saturated temperatures of heat exchangers, ORC boiler superheat, pinch points and the sub-cooling of both the ORC and VCC condensers. Then, multi objective optimization was performed using Genetic Algorithm followed by multi-criteria decision-making using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to select the best optimal solutions from the Pareto front. Sixteen combinations comprising of four working fluids, R245fa, R600, R600a, and R134a were analyzed. The optimization results showed R245fa ORC-R600a VCC to be the best candidate at ambient conditions of 30°C, while at 35°C and 40°C, R600a ORC-R245fa VCC was found to be the most suitable candidate. [ABSTRACT FROM AUTHOR]

Highlights • Analysis of AC use behavior of residential occupants by physical environment factors. • Various behavior information was collected through IR signal of AC RC. • Three prediction algorithms (LR, RF, SVM) were applied to predict AC on–off state. • Each model is cross validated using 3 criteria (F -measure, Kappa, on–off frequency). Abstract This study analyzed behaviors of occupants in residential buildings with a focus on physical environmental elements. To this end, collection and analysis of Air Conditioner (AC) use behavior data, as well as the prediction and validation of AC on–off state were conducted. For the collection of behavioral data collection, IR signals of AC remote were collected, based on which various AC adjustment behaviors (on–off, set-point temperature) that determines energy use, use rate, and set-point temperature were analyzed. Using the analysis, the AC use behavior was defined as on–off state prediction, and the difference between indoor and outdoor temperature was suggested as the standard for expressing AC use rate. Further, analyzing the duration of AC operation for each set-point temperature revealed that there are differences in AC operation propensities of respective units. Next, the collected data was used to predict and test AC on–off state. Three prediction algorithms (Logistic Regression, Random Forest, and Support Vector Machine) were applied, and the results of the cross-validation was evaluated using three criteria: F -measure, Cohen's Kappa, on–off frequency. Results showed superior prediction performance of Random Forest. Despite the importance of resident behavior studies, there has been only a limited number of studies conducted on residential AC use behavior. The research process and analysis method in each step of this study is expected to be helpful for researchers hoping to conduct behavioral studies in the future. [ABSTRACT FROM AUTHOR]

Highlights • A 12 month mixed-method longitudinal study of energy demand in Kuwaiti villas. • Building energy models grounded with physical and social empirical data. • Space cooling accounts for 50% to 75% of total energy use in the case study villas. • Air conditioning thermostat settings are a key driver of energy use. • Insulation and double glazing contribute to more uniform internal temperatures. Abstract This paper investigates the physical and social determinants of energy use in four case study villas in Kuwait. The investigation consisted of a 12 month mixed-method longitudinal monitoring and surveying study followed by building energy modelling. Monitoring and surveying provided empirical data about household energy use, internal temperatures, building and system characteristics and occupant energy-use behaviours. This data was used to ground models to better predict energy use. Models grounded with empirical data show that space cooling accounts for 50% to 75% of total energy use in villas. Results suggest that drivers of cooling energy are varied, with 30% of cooling energy in some villas attributable to heat gains from appliances. Furthermore, while the study found no distinct reduction in measured and modelled annual energy use intensities of villas with improved levels of efficiency (insulation and double glazing), such measures did contribute to more uniform internal temperatures. The study also identified key occupant energy-consuming behaviours including a tendency not to adjust air conditioning (AC) thermostat settings during periods of summer travel and lack of regular AC maintenance. The approach and findings of the study can inform future studies in Kuwait seeking to understand energy use in a nationally representative sample. [ABSTRACT FROM AUTHOR]

Highlights • A thermoelectric cooler-heat pump is proposed for the HVAC of nZEBs. • A novel methodology to thermally characterize heat exchangers is proposed. • The thermal resistances of the heap pipes working contrary to designed duplicate. • A validated computational model simulates the thermoelectric behavior. • The number of modules has to be optimized to achieve the optimum COP. Abstract The nZEB standards reduce the energy demand of these buildings to a minimum, obtaining this little energy from renewable resources. Taking these aspects into consideration, a thermoelectric cooler-heat pump is proposed to achieve the comfort temperature along the whole year. The same device can provide heat in winter and it can cool down the buildings in summer just by switching the voltage supply polarity. Heat pipes are studied to work on both sides of the thermoelectric modules in order to optimize the heat transfer as these devices present really good thermal resistances and they can work in any position. However, they present pretty different thermal resistances if they work on the cold or on the hot side of the modules. A methodology to thermally characterize these heat exchangers working in both orientations is proposed and a validated computational model is developed to optimize the thermoelectric cooler-heat pump for a nZEB application. The number of thermoelectric modules, the position of the device, the ambient temperature and the air mass flow determine the operation and consequently they need to be studied in order to optimize the application. [ABSTRACT FROM AUTHOR]

Abstract With growing health risks from rising temperatures in the Global South, the lack of essential indoor cooling is increasingly seen as a dimension of energy poverty and human well-being. Air conditioning (AC) is expected to increase significantly with rising incomes, but it is likely that many who need AC will not have it. We estimate the current location and extent of populations potentially exposed to heat stress in the Global South. We apply a variable degree days (VDD) method on a global grid to estimate the energy demand required to meet these cooling needs, accounting for spatially explicit climate, housing types, access to electricity and AC ownership. Our results show large gaps in access to essential space cooling, especially in India, South-East Asia and sub-Saharan Africa. Between 1.8 to 4.1 billion, depending on the required indoor temperatures and days of exposure, may need AC to avoid heat related stresses under current climate and socio-economic conditions. This number far exceeds the energy poverty gap indicated by the Sustainable Development Goal for electricity access (SDG7). Covering this cooling gap would entail a median energy demand growth of 14% of current global residential electricity consumption, primarily for AC. Solutions beyond improved AC efficiency, such as passive building and city design, innovative cooling technologies, and parsimonious use of AC will be needed to ensure essential cooling for all with minimized environmental damage. Meeting the essential cooling gap, as estimated by this study, can have important interactions with achieving several of the SDGs. [ABSTRACT FROM AUTHOR]

Highlights • A nominal output capacity based performance index for ASHPs, ɛ NL , was proposed. • ɛ NL was influenced by ambient air conditions and defrosting initiating time. • A four-layer GRNN model for predicting ɛ NL was developed. • ɛ NL at varying operating conditions was predicted using the GRNN model. Abstract Air source heat pump (ASHP) units have been widely used for space heating in recent years. While a space heating ASHP unit is normally rated at its nominal operating condition, however, during its actual space heating operation, it rarely works at the nominal condition. The actual output heating capacity can remarkably deviate from that at the nominal condition, due to the influences of ambient air temperature and frosting-defrosting operation. Therefore, to enable a comprehensive and convenient evaluation of the operating performance of ASHP units with frosting-defrosting operation and to provide designers with appropriate design guidelines to size a space heating ASHP unit, a new performance index has been proposed. The new performance index, ɛ NL , or the loss coefficient in the nominal output heating energy, was actually based on the nominal output heating capacity of ASHP units, which was readily available and stayed unchanged irrespective when and where ASHP units were operated. In this paper, the definition of ɛ NL was firstly given, following a detailed explanation of the frosting-defrosting operation of an ASHP unit. Secondly, a GRNN model for predicting the ɛ NL of a field ASHP unit was established following a correlation analysis using a total of 473 groups of field measured data from the field ASHP unit. Finally, a modeling study using the developed GRNN model was carried out, and the study results suggested that when an ASHP unit was operated at a fixed ambient condition, defrosting initiating time would affect the ɛ NL , and there may exist an optimal defrosting initiating time at which ɛ NL was at its minimum. The model results also demonstrated that an increase in ambient air relative humidity or a decrease in ambient air temperature would result in an increase in ɛ NL , when an ASHP unit was operated with a fixed defrosting initiating time. [ABSTRACT FROM AUTHOR]

Highlights • The energy performance of all variable-speed packaged units needs to be evaluated. • Field test and simulation are combined in the performance evaluation. • The evaluated unit has > 30% higher cooling efficiency than most units in the field. • All variable-speed units can save 22%−29% electricity than the code requirement. Abstract Packaged air-conditioning units are widely used in commercial buildings. Most packaged units in the field are equipped with constant-speed supply fans and constant-speed compressors. Many efforts have been made to increase the operational efficiency of packaged air conditioners by retrofitting existing units with improved controls or developing innovative products with advanced designs. This paper evaluates the energy performance of a packaged unit with all variable-speed components, including the supply fan, the compressor, and the condenser fan. Both a field test and energy simulation are used to quantify the potential of energy savings from the variable-speed unit (VSU) against reference units. In the field test, the reference is a six-year-old existing unit. It was determined through monitoring for one year that the VSU had a 31% higher cooling efficiency than the reference unit. In the simulation, a retail building model was used to compare the performance between the VSU and two reference units—one representing existing units in the field and the other representing units meeting the latest energy code requirements by ASHRAE Standard 90.1. Simulation results showed that depending on the climate, the VSU can save electricity consumption (including both cooling and fan) by 45%–54% relative to the first reference and by 22%–29% relative to the second reference. These findings are valuable for building designers and contractors in selecting packaged rooftop units for both new constructions and retrofits. [ABSTRACT FROM AUTHOR]

Highlights • An enhanced PCA sensor fault detection method was presented for screw chillers via EEMD. • EEMD was applied for signal decomposition and noise elimination of raw sensor measurements. • 11 sensors were chosen for PCA modeling by quantifying the chiller energy performance. • Field operating data from real screw chillers at summer seasons were used for validation. • EEMD significantly improved the PCA-based sensor fault detection performance. Abstract In heating, ventilating and air conditioning (HVAC) systems, sensor faults cause improper control strategy resulting in both energy penalty and service costs. As the first and foremost step of an entire on-line fault-tolerant control strategy, sensor fault detection is crucial for maintaining system operation performance. Principal component analysis (PCA) is a widely studied sensor fault detection method in HVAC area. However, the noise information contained in field sensor measurements may confuse the PCA model training process and deteriorate the sensor fault detection performance. To address the problem, this study presented an enhanced PCA-based sensor fault detection method using ensemble empirical mode decomposition (EEMD) denoising. The proposed EEMD-PCA method includes two steps: (1) EEMD is adopted to decompose and denoise the raw measured data, namely eliminating the possible noise information and extracting the possible useful information; (2) PCA is employed to establish the Q -statistic with threshold for sensor fault detection. A case study on a real screw chiller system was conducted to validate the proposed method. 11 sensors were chosen for modeling by qualifying chiller energy performance. Field operating data were used for validation with various magnitudes of added biases. Results revealed that EEMD-PCA showed better detection performance than PCA for 8 critical sensors. The fault detection ratios of the 8 sensors were increased by 22% at average. After pre-processed by the EEMD method, the denoised data were smoother than the raw data. Data distributions in the PCA residual subspace indicated that EEMD-PCA is sensitive to the smaller biases since the denoised data can separate faulty data from the normal data. [ABSTRACT FROM AUTHOR]

Abstract Taking advantage of the thermal inertia of a building to shift the cooling load during a demand response (DR) period is an important strategy for commercial buildings. However, researchers have consistently ignored the effects of the thermal inertia of air-conditioning systems on the DR potential of buildings. This study focused on the coupling effect of a building and an air-conditioning system. This paper describes the construction of a coupled thermal inertia model of a building and an air-conditioning system, which was validated experimentally; analyzes the accuracy of the thermal inertia of a building solely, as well as the accuracy of both the building and air-conditioning system together by comparing the model and experiment results; and quantifies the DR contributions of the thermal inertia of both the building and the air-conditioning system. A considerable demand reduction effect of the thermal inertia of the air-conditioning system was found, and the results of coupling the thermal inertia of both the building and air-conditioning system showed greater accuracy. [ABSTRACT FROM AUTHOR]

Abstract Our goal in this contribution is to study and formulate different scenarios that will aid in identifying opportunities to increase environmental and energy sustainability of the urban environment of a tropical coastal city. These cities are among the most vulnerable to climate change and impacts on the energy infrastructures are particularly concerning. For this effort, the Weather Research and Forecast (WRF) model, with the Building Effect Parameterization and Building Energy Model (BEP-BEM) schemes are applied to the tropical coastal San Juan Metropolitan Area (SJMA) in Puerto Rico. SJMA has a total population of 2.0 million inhabitants in a total area of 750 km2. Building use and heights required for BEP-BEM are estimated from the World Urban Database Access Portal Tool (WUDAPT), Local Climate Zones (LCZs) classification, which partitions the urban landscape into ten different urban classes. WUDAPT output was compared with observations from the NASA ATLAS Mission and NASA Landsat 8 imagery, and was found to be in good resemblance for their urban built up class. To test energy use and mitigation strategies for SJMA, we consider the heat wave event from 09-29-2014 to 10-02-2014. The new modeling methodology (BEP-BEM-WUDAPT) was found to improve results from non-urbanized versions when compared with observations from a network of weather stations. Air conditioning (AC) demand from urban WRF was found to be in good agreement with EnergyPlus™ simulations and available records from the local power utility company. The work explored building energy sustainability and mitigating options to include energy efficient building materials, white roofs, increase of indoor AC temperature set points, and use of high efficiency AC equipment. Among mitigating alternatives explored, results show that white roofs, target indoor temperatures and high efficient AC systems reduce the total energy demand by 6.9%, 9.9% and 16.15%, respectively. Furthermore, when considering all alternatives simultaneously, the peak demand decreased by 40% during a tropical heat wave event. [ABSTRACT FROM AUTHOR]

Abstract A field study of thermal environment and thermal comfort under air carrying energy radiant air-conditioning system (ACERS) and split air conditioner was conducted in south-central China. The results reveals that the ACERS can create a comfortable thermal environment with small vertical temperature gradient. Based on the regression equation of thermal sensation vote and operative temperature, it is found that the neutral temperature in ACERS is about 28 °C in summer and 18 °C in winter, while in split air conditioner, the temperature is about 26.1 °C and 19.3 °C. Besides, the comfortable temperature range for 80% acceptability in ACERS is 25.5–30.7 °C in summer and 13.8–22.2 °C in winter, and the corresponding range of split air conditioner is 23.9–28.3 °C and 16.7–21.8 °C. The results show that the ACERS has a larger temperature adjustment range compared to the split air conditioner and the thermal comfort stability of ACERS is better. The actual comfortable temperature range is wider than the theoretical value calculated by PMV-PPD equation, and the reason may be that people in their long-term life have been adaptable to the environment. This research is helpful for the design and operation of air-conditioning system in future and provides the theoretical basis for indoor temperature setting in this area. [ABSTRACT FROM AUTHOR]

Traditional fresh air handling units with exhaust air heat recovery insufficiently utilize exhaust air waste energy, and are designed under the design-load condition, which are unable to efficiently adapt to the varying fresh air conditions for both the energy consumptions of compressors and fans. To promote energy efficiency, a fresh air handling unit with exhaust air heat recovery is constructed in this study by grade matching of loads and waste and natural energies under different typical fresh air parameter conditions, and the improved year-round efficient system configuration is determined by combining the configurations obtained under each typical condition. In addition, numerical models are adopted to calculate the energy and economic performances of the system in an office building in Nanjing, China, and a traditional system is also analyzed for performance comparison. The main conclusions are as follows: 1) The improved system configuration is determined to be a heat-recovery device plus a two-stage heat pump that can operate close to a separately designed configuration under various cases, thereby ensuring efficient operation throughout the year; 2) Compared with a traditional fresh air handling unit for fresh air load treatment with a variable refrigerant flow system in Nanjing, China, the annual saving rate of the proposed system is 14.4 %; 3) Compared with the traditional system, the static payback period is 1.1 years. [ABSTRACT FROM AUTHOR]

• An integrated air conditioning system (IACS) is proposed for a passive house. • A machine learning model is developed to quantify thermal comfort. • Multi-objective optimization of operating conditions for the IACS is presented. • The IACS consumes less energy under optimal operating conditions. • The IACS is economical, particularly in a passive house. Appropriate dehumidification systems are essential for passive houses to prevent thermal discomfort under high dehumidification loads. In this study, an integrated air conditioning system (IACS) consisting of a dedicated outdoor air system and sensible cooling unit is developed to manage dehumidification loads in passive houses. Multi-objective optimization of the operating conditions of the IACS is performed considering the energy consumption and thermal comfort of a passive house. A thermal comfort model is constructed using a big-data-driven machine learning method to improve prediction accuracy compared to a conventional model. Additionally, an artificial-neural-network-based metamodel is developed to estimate the performance of the IACS. The optimal operating conditions are determined based on the Pareto solution set obtained from multi-objective optimization. The optimized performance and economic feasibility of the IACS are compared to those of a conventional air conditioning system (CACS). For the IACS in a passive house, the optimal indoor set-point temperature and relative humidity were determined to be 27.01 ℃ and 50.35%, respectively. The energy-saving rates of the IACS were 15.96% and 7.53% in passive and normal houses, respectively, compared to the CACS. The IACS is more economical than the CACS based on its energy-saving effects, particularly in passive houses. [ABSTRACT FROM AUTHOR]

The heat released from air-conditioning units is a cause to deteriorate the thermal environment around buildings, which is more serious around multi-story residential buildings. It is most effective to directly reduce the released heat from the air-conditioning unit in the mitigation of the outdoor temperature rise caused by the hot air from the air-conditioning unit. This study proposed a water spraying box as a simple and economic measure to mitigate the released heat from the air-conditioning unit. The cooling effect of the proposed water spraying box was investigated using experimental mock-ups. The experiment shown that the water spraying box installed on the air-conditioning unit can lower the exhaust hot air by about 10 °C. Numerical simulations were conducted to analyze the air temperature reduction around the buildings when the proposed spraying boxes were installed in multi-story residential buildings. The simulation results revealed that the average temperature of outdoor air around the building can be lowered by a maximum temperature of 2 °C when all air-conditioning units in the building were installed with the proposed water spraying box. [ABSTRACT FROM AUTHOR]

To address the problem that the energy consumption of chiller is affected by multiple factors and lacking of abnormal detection model which can be used for energy consumption of chillers, an integrated model based on Partitioning around medoids(PAM), Support Vector Classification (SVC) and 3-σ rules is proposed in this study. In the detection of abnormal energy consumption of the chillers, the observed data of a Heating Ventilation and Air Conditioning (HVAC) system from Hubei province, China are used to develop and evaluate the integrated model. PAM is applied to divide consumption patterns of the historical data. SVC is applied to recognize consumption patterns of the testing data. 3-σ rules are used for abnormal detection. The results show that PAM divide the energy consumption into four patterns, SVC multi-classifier can effectively recognize energy consumption patterns with an accuracy reaches 98.29%. Compared with the popular model, the integrated learning model applied for the detection of abnormal energy consumption reaches a twice as high as the 40% detection success rate of the 3-σ rules. Moreover, the patterns recognition component can be used to understand the people's energy consumption behavior and the anomaly detection component can be used to regulate the energy usage. [ABSTRACT FROM AUTHOR]

[Display omitted] • The LCA methods and findings of 41 papers on air-conditioning systems are examined. • Retrieved studies presented discrepancies in the LCA goals and scope definition. • The global warming potential (GWP) was the most evaluated indicator across papers. • Grid-connected electrical heat pumps generally had a higher life cycle GWP. • Improvements related to AC energy sources and other life cycle aspects are proposed. Air conditioning (AC) has become the fastest-growing energy end-use in buildings worldwide, and its adoption is expected to increase further due to various socioeconomic factors and climate change. Despite this, the decarbonization of space cooling is progressing slowly; hence, the emissions burden at each stage should be examined in detail. Given this context, it is crucial to assess the environmental performance of AC systems by considering a comprehensive approach like the Life Cycle Assessment (LCA). While several studies have already targeted the LCA of AC systems, a clear overview of those studies' methodologies, assumptions, and results is still lacking. Therefore, this systematic review examined the methods and key findings of 41 peer-reviewed articles. Although the results revealed discrepancies among the goals and scope, impact assessment methods, and indicators, most articles predominantly focused on the global warming potential (GWP) impact, and its reported quantification was further analyzed. It was observed that grid-connected vapor-compression heat pumps/chillers (conventional systems) presented higher GWP impacts than other AC systems powered by renewables, which in contrast, exhibited higher impacts in some toxicity and resource depletion-related indicators. However, the share of GWP showed different distributions, as the pre-operation emissions of solar-powered AC systems (either PV or thermal) were higher than those of conventional systems, mainly driven by reduced energy consumption usage. This review concludes with some recommendations for each stage of the life cycle. Since the operation phase generally showed the highest impact, more efforts should be devoted to accelerating the transition to a 100% clean electricity power system at a national scale. In contrast, the need to power AC systems with on-site renewables should be carefully assessed based on the context. [ABSTRACT FROM AUTHOR]

Building energy flexibility has been increasingly demonstrated as a cost-effective solution to respond to the needs of energy networks, including electric grids and district cooling and heating systems, improving the integration of intermittent renewable energy sources. Adjusting zonal temperature set-points is one of the most promising measures to unlock the energy flexibility potential of central air conditioning systems in complex commercial buildings. However, most existing studies focused on quantifying the energy flexibility on the building level since only building-level energy consumption is normally metered in today's commercial buildings. By better understanding zone-level energy flexibility, building managers can design more targeted energy flexibility strategies, balancing overall flexibility potential with costs to occupants. This study aims to investigate the impacts of temperature set-point adjustment strategies on zone-level thermal and energy performance by developing a non-intrusive data-driven load disaggregation method (i.e., a 'virtual' zonal power meter). Three university buildings in Northern California (containing 136, 217, and 142 zones) were selected to test the proposed load disaggregation method. Zonal temperature set-point adjustment strategies were previously tested in these buildings. We found that heterogeneities of energy use and energy flexibility existed across not only buildings but also air handling units (AHUs) and zones. Moreover, a small number of zones accounted for a large amount of energy use and energy flexibility; and the most energy-intensive zones are not necessarily the most energy-flexible zones. For the three tested buildings, the top 30% most energy-intensive zones accounted for around 60% of the total energy use; and the top 30% most energy-flexible zones provided around 80% of the total energy flexibility. We also found the effects of temperature set-point adjustment on indoor air temperature were limited and heterogeneous. The proposed virtual zonal power meter enables the electric grid or district energy system operators to regard the controlled energy-flexible entities as a fleet of AHUs or zones instead of a fleet of buildings and helps unlock the possibility for targeted demand flexibility strategies that balance zone-by-zone energy reduction with zone-by-zone costs to occupants. [ABSTRACT FROM AUTHOR]

Residential Heating, Ventilation, and Air Conditioning (HVAC) systems are progressing towards smarter climate control solutions, centered on the user needs and the system's energetic performance. This work introduces a strategy to assist households select more energy-conscious HVAC setpoints without compromising their comfort level. To do so, it explores the adaptive comfort theory for homes combined with predictive indoor temperature models to define an adaptable strategy for optimized indoor temperature setpoints. Using smart thermostat data from European households, energy savings up to 5.2% were estimated from adopting the new thermostat setpoint schedules in several test cases for the heating season. The proposed methodology is independent of direct information about building construction or personal information, which is often unfeasible to measure and collect in real installations. The solution complexity was taken into account, looking for its application in smart thermostat devices with constrained computational capabilities. The Multi-Layer Perceptron (MLP) predictive models achieved performances with Mean Squared Errors (MSEs) lower than 0.06. The same were used to evaluate the setpoints' impact on the indoor temperature, with simulation errors lower than 1.1 ∘C. [ABSTRACT FROM AUTHOR]

• A novel circle diffuser with high jet length and low flow resistance was introduced. • A comprehensive performance evaluation index, ADEISW, was proposed to describe the performance of circle diffusers under different operating conditions. • The ADEISW of the novel circle diffuser is 62.90 % smaller than the traditional circle diffuser. The energy consumption of buildings in China accounts for approximately 20 % of the total national energy consumption, with heating, ventilation, and air conditioning (HVAC) energy consumption accounting for approximately 60 % of building energy consumption. This study focused on circular diffusers in ventilation and air conditioning systems. Based on Radial Basis Function (RBF) surrogate model prediction and multi-island genetic algorithm optimization, the structural form of circular diffusers was optimized, and a comprehensive evaluation index for winter diffuser performance was proposed. The influence of different air velocities on the Air Diffuser Effective Index for Summer and Winter (ADEISW) of circular diffusers was analyzed, and the implementation effect of the optimized novel circular diffuser was verified through full-scale experiments. The results show that by using the RBF surrogate model for prediction and the multi-island genetic algorithm for optimization, the optimal parameters for the novel circular diffuser, including the position, number, and shape of the guide vanes, were obtained, and the performance coefficient was ultimately reduced to 1.37. The experimental results at different wind speeds showed good agreement with the numerical simulation results, confirming the effectiveness of the optimized circular diffuser. The proposed optimization approach has the potential to yield practical design guidelines and solutions for local component structural optimization challenges encountered in the ventilation and air conditioning sector. [ABSTRACT FROM AUTHOR]

The traditional method for calculating the cooling load of stratified air-conditioning (STRAC) systems is complicated, particularly for interzonal heat transfer (IZHT). This study adopted experimental and computational fluid dynamics (CFD) methods to analyze the IZHT at two model scales and proposed a novel cooling load calculation method for STRAC systems. This new method, which is based on the equivalent heat transfer coefficient K c , is called the interzonal thermal resistance method. According to the CFD simulation results of the full-scale model, the equivalent heat transfer coefficient K c of the occupied and unoccupied zones ranged from 42.66 to 44.36 W/(m2·℃) under the nozzle sidewall air supply (NSWAS) system. Based on the magnitude of the coefficient, the difference between the cooling load calculated by the newly developed and traditional methods was only 4.16%, which proves the reliability of the interzonal thermal resistance method. Moreover, compared to the traditional method, the new method is more convenient. This method provides an alternative for calculating the cooling load of STRAC systems in practical engineering applications with a solid theoretical foundation. [ABSTRACT FROM AUTHOR]

In this paper the results of a wide investigation on a geothermal-integrated air HVAC system are reported: in the first part, the energy performances of an Earth to Air Heat eXchanger (EAHX) operating in Pomezia (a locality of the area of Rome, Italy) are carried out through a comprehensive numerical two-dimensional model based on the finite element method and the system is forced under real time-changing weather conditions over 1 year. The model is able to reproduce the time- and space- dependent thermo-fluid-dynamic behavior of the EAHX system, the humid air flux and the surrounding soil, as well as to account for the mass of condensed water in the tubes. In the second part of the investigation, the EAHX is coupled to an air handling unit of a company building for office use and tested while operating both in cooling and heating mode. The hourly results, in terms of power savings and energy consumption were reported for representative days of all the months belonging to the cooling or the heating working seasons, monthly and seasonal assessment. The convenience in terms of seasonal energy savings is +40.8% in cooling mode and +44.1% in heating mode. Then, the economic impact in terms of payback time and net present value was investigated through a cost analysis to examine the return of the economic investment under two possible soil morphologies and different prices of electrical energy in Italy. The simple payback with the real price of energy in Italy (0.50 €/kWh) is 3.3 years for the tufa-soil and 5.8 years for soft rock-soil; the economic return in terms of net present value is around 500 k€ for both the morphologies (560 k€ for the tufa; 446 k€ for the soft rock), per effect of the energy saving and, consequently, for a reduced expense of electrical energy. Therefore, in all the considered cases the installation of EAHX systems in support of traditional systems would generate both energy and economic advantages. [ABSTRACT FROM AUTHOR]

• The effect of the inaccurate load label on prediction models is analyzed. • The method of extracting load from cooling capacity is proposed. • Practical cases are used for validation. • The accuracy of ultra-short-term load prediction models is improved. Data-driven modeling achieves excellent performance in building load prediction by mining the actual load characteristic from operation data. When predicting room air-conditioning load, however, the measured data of cooling capacity cannot be used as the load label in the modeling as customary. The reason lies in the unstable automatic control of the air-conditioner, which makes the cooling capacity oscillate at the actual load. The mismatch of cooling capacity and actual load will affect prediction modeling. This paper first sets up a simulation experiment to quantitatively analyze this effect. The results indicate that the more the cooling capacity deviates from the load, the greater the error of the prediction model. To solve this problem, a load extraction method based on the Fourier decomposition is proposed, which involves transforming the actual cooling capacity data into the frequency domain and extracting the low-frequency components as load data, which is further used for modeling. The experiments show that the accuracy between extracted data and real load exceeds 90%. Additionally, the load extraction method improves the prediction accuracy by up to 17.55% compared to the model using cooling capacity data directly. Finally, a practical case demonstrates the effectiveness of the proposed load extraction method. [ABSTRACT FROM AUTHOR]

• A radiant-floor cooling system using geothermal energy was evaluated. • Shallow geothermal energy can condition outdoor air for radiant-floor cooling. • The dehumidification ability of the system under specific conditions was analyzed. • Earth-air heat exchangers provide energy-efficient cooling and dehumidification. Radiation floor cooling systems (RFCSs) have no dehumidification capability and must be used with air handler systems. During summer, the shallow geothermal energy temperature is lower than the outdoor air dew point temperature. Using shallow soil for the heat exchange with outdoor air can effectively reduce the temperature and humidity of the outdoor air. Hence, using an earth–air heat exchanger (EAHE) instead of an air-handler unit (AHU), combined with RFCS, can eliminate the floor surface condensation. A physical simulation test bench was built, and RFCS and EAHE were first studied separately to observe the temperature and humidity characteristics of the room. Then, the two systems were coupled and the performance of the combined system was evaluated. The results reveal that the EAHE system can effectively reduce the air-humidity ratio in a room. The test data show that the temperature of the floor surface is approximately 26.5 °C, while the humidity ratio of the floor surface is significantly reduced to approximately 16 g/kg when the composite system is operated for 1 min. When the composite system runs for 30 min, the room temperature at heights below and above 0.5 m is within 22–23.5 °C and 23.5–24.5 °C, respectively. The humidity ratio of the floor surface is 12.74 g/kg. Hence, the composite system can considerably reduce the temperature and humidity of the room in the initial stage of operation. This research provides baseline information for addressing the challenges encountered with floor surface condensation. [ABSTRACT FROM AUTHOR]

• An ontology-based automatic building energy modeling (BEM) framework is developed. • The proposed ontology model (OM) integrates four key domains for BEM. • OM supports automatic thermal zoning by cross-domain reasoning. • Dynamic data can be translated to BEM facilitated by OM. • The OM to BEM approach saves model development time by 99%. Building Energy Modeling (BEM) has wide application in building design and operation stages. Developing BEM requires numerous data from multiple sources, and therefore is time consuming and expertise demanding. Building Information Modeling can provide information needed for BEM, but a seamless transfer of BIM to BEM is not available yet. This study proposes an ontology-based automatic framework aimed at integrating multiple data sources and automatically generating BEM models, in which the ontology model could integrate data from weather, building, internal heat gain, and Heating, Ventilation, and Air Conditioning system. We developed a cross-domain and rule-based reasoning method for thermal zoning, and an automatic ontology model to BEM model translation method using instance-based mapping and dynamic data conversion. We demonstrated our approach by developing a BEM model for one floor of a campus building. The results showed that the proposed framework could automatically generate the model that has the potential to reduce the modeling time by over 99% comparing with manual modeling. [ABSTRACT FROM AUTHOR]

[Display omitted] • Thermal comfort affects human health, productivity, and well-being. • Existing related models are difficult for intelligent controlling HVAC systems. • Physiological parameters and psychological factors are precursors of thermal comfort. • The human factors mechanism has not been fully elucidated. • Human factors can help achieve on-demand intelligent control of HVAC systems. The indoor thermal environment in buildings, industry, and other fields greatly affects human thermal comfort, productivity, energy consumption, and even health. The heating, ventilation, and air conditioning (HVAC) system is the primary way to regulate the indoor thermal environment. However, the local thermal discomfort phenomenon is widespread nowadays. With the increasing demand for intelligent control of the indoor thermal environment on demand, it is necessary to clarify the thermal comfort demand law of the human body. Note that, existing related studies mainly focus on the thermal comfort model, the thermal sensation model, and the thermophysiological model. And researchers tend to consider physiological parameters in these models rather than directly considering psychological factors. But the objective physiological parameters and psychological factors are precursors of thermal comfort, which directly determine the human thermal comfort level. In addition, the human factors relationship between objective physiological parameters, psychological parameters, behavioral parameters, and environmental parameters has not been fully elucidated. Therefore, it is difficult to achieve truly intelligent regulation. This investigation provides new insights for future research directions on human factors engineering for on-demand intelligent control of HVAC systems, which helps to build a healthy, comfortable, productive, low carbon, and energy-saving indoor environments in closed/semi-closed spaces. [ABSTRACT FROM AUTHOR]

• An integrated energy recovery ventilator (ERV) with a cooling coil is proposed. • Field measurements for conventional and proposed ERV systems are conducted. • Limited latent capacity of conventional ERV systems can cause high indoor humidity. • Proposed ERV system yields dehumidification load reduction in varying conditions. • Optimum operating conditions are determined based on thermal comfort and energy. This study aims to provide a ventilation solution for energy-efficient buildings that suffer from high indoor moisture content during cooling seasons. With the decreased sensible load in highly insulated buildings, conventional energy recovery ventilators (ERVs) and air conditioners are inadequate to effectively handle the increased dehumidification load. Moisture removal through air conditioners requires cooling the air below its dew point, which makes precise control of indoor humidity difficult and reduces the level of indoor thermal comfort. Thus, an ERV system integrated with a cooling coil (ERV-CC) is proposed to enhance its latent heat effectiveness. Field measurements were conducted for the ERV and ERV-CC systems to compare their energy performances and levels of indoor thermal comfort. The results indicated that the ERV system did not satisfy the recommended range of indoor humidity even when the set-point temperature of the air conditioner decreased. However, the ERV-CC system could condition the indoor air within the comfort zone at higher energy efficiencies. In addition, the ERV-CC system was observed to benefit from the dehumidification effect through the enthalpy recovery process under a wider range of outdoor conditions. Especially, at low outdoor temperatures, the ERV-CC system dehumidified the incoming fresh air, whereas the ERV system humidified the fresh air. Overall, the optimum operating conditions for the ERV-CC system were determined to be a set-point temperature and relative humidity of 27 °C and 50%, respectively, while satisfying the thermal comfort criterion at an 8.24% predicted percentage dissatisfied index with minimum energy consumption. Furthermore, the ERV-CC system not only reduced the building load, but also improved the individual air conditioner performance. [ABSTRACT FROM AUTHOR]

In the era of post-Coronavirus Disease 2019, the dedicated outdoor air system (DOAS), which provides 100% outdoor air for the building, is widely acknowledged as it can ensure acceptable indoor air quality by delivering fresh outdoor air to occupied space. The DOAS with a proper design and operation can provide sufficient ventilation and dehumidification while achieving energy efficiency. Nonetheless, there is limited guidance in determining the optimal control sequence of the DOAS for the designers and operators to implement in practice. Accordingly, in practice, a number of issues have been acknowledged in the design and control phases of DOAS, including insufficient ventilation and dehumidification, and increasing supply air dry-bulb temperature in fear of over-cooling, which might cause significant discomfort and energy waste. There have been efforts to develop high-performing DOAS controls for better energy efficiency. However, such controls are often complex, or difficult to interpret, for building designers and operators to consider in practice. In this regard, this paper explores a simulation-based framework for generating a supply air temperature control sequence of the DOAS not only to ensure improved energy-saving potential but also to guarantee the implement-ability of the control logic. The U.S. Department of Energy prototype primary school with dynamic occupancy profiles was modeled with a whole building simulation program, EnergyPlus. The model consists of a DOAS with an exhaust air energy recovery system for ventilation and fan-coil units for space cooling and heating. Then, a Genetic Algorithm was adopted to find the true optimal supply air temperature control sequence in terms of minimizing the energy cost of the heating, ventilation, and air conditioning system operation. Lastly, Decision Tree was adopted to extract rules out of the optimums to derive an implementable sequence of operation for the DOAS supply air temperature. A total of 12 week-simulation including four weeks of heating, cooling, and shoulder seasons, separately, under the weather condition of New York City was conducted for the case study. This case study identified that the optimization-informed rule extraction-based control, when compared to conventional outdoor air temperature-based reset control, could save about 13% of energy cost and 25% of energy consumption throughout the heating, cooling, and shoulder seasons. It is notable that the energy-saving was mainly achieved by reducing the heating energy consumption. Importantly, it nearly corresponds to the true optimal control result, which reduces approximately 14% of energy cost and 27% of energy consumption. From the results, it can be highlighted that the optimization-informed rule extraction can be as energy effective as the optimal control, while significantly reducing the complexity of the control. [ABSTRACT FROM AUTHOR]

• A new air conditioning system with double evaporation temperature is proposed. • To obtain the maximum value of dehumidification and cooling capacity, COP, the cylinder volume ratio of compressor is 0.5∼1.3. • The cooling capacity and cooling COP are 1.22%∼4.29% and 1.33%∼4.54% higher than that of single evaporation temperature system. In order to improve the cooling and dehumidification performance of household air conditioners, a new air conditioning system with double evaporation temperature is proposed. The simulation model of the new system is established, the variation of system performance with compressor high/low pressure cylinder volume ratio, inlet air temperature, relative humidity, air speed, etc. are simulated and experimentally studied. The research results show that when the volume ratio of high and low pressure cylinders of compressor is 0.5∼1.2 and air speed 2.2 m/s, cooling COP of double evaporation temperature air conditioning system could obtain the maximum value. Compared with single evaporation temperature system, cooling COP of double evaporation temperature system can be increased 3%∼4.2%; the dehumidification capacity is 0.06∼0.61 kg/h, about 1.69%∼5.83% higher. [ABSTRACT FROM AUTHOR]

Energy conservation and carbon reduction of existing buildings have been receiving increasing attention with the proposed carbon neutrality goals. As presented in many studies, adopting model predictive control (MPC) for building HVAC system control is an effective means to realize building energy conservation. However, existing studies rarely addressed the critical challenges for practical applications and integration of MPC in existing Building Automation Systems (BASs). This study proposes a control retrofit approach, which is lightweight and replicable, to realize the improvement of energy efficiency of the existing building HVAC systems through integrating data-driven MPC into existing BASs. The critical challenges in the practical applications of MPC strategies, including MPC strategy development, large-scale data processing, strategy deployment and integration with existing BASs are addressed. The proposed approach was applied to the existing HVAC system of an actual airport terminal and the evaluation results indicate that the control retrofit approach is effective in achieving energy efficiency and thermal comfort improvement. The system daily energy consumption was reduced by 24.5% in average and the percentage of the discomfort time was reduced from 70.2% to 5.7% in average after the control retrofit. The proposed control retrofit approach, with the help of the scalability and distributed computing capabilities of the cloud computing platform, is expected to be able to realize lightweight control retrofit of large number of existing building HVAC systems. [ABSTRACT FROM AUTHOR]

• A gray-box energy model for residential ACs is developed, calibrated, and validated. • State variables (evaporation and condensation temperatures) are keys in the model. • Heat transfer at condenser and evaporator correlate state variables to model inputs. • Seven performance properties functions are identified for model calibration. • The cooling capacity and electrical power input can be accurately predicted. Energy models for vapor compression refrigeration in residential air conditioners have been developed through white-box, gray-box, and black-box methods in decades. However, existing white-box and gray-box models require complicated equations with detailed geometries while black-box models require substantial experimental data. This paper aims to develop and validate a simple gray-box steady-state energy model without the need for detailed geometries, which can accurately predict the cooling capacity and electrical power input based on outdoor and indoor air conditions, and supply airflow rates. First three state variables, including the evaporation and condensation temperatures, and refrigerant mass flowrate, are applied to develop the energy model and are solved by three physical equations, including the energy conservations at the evaporator and condenser, and the refrigerant volume-mass flow correlation of the compressor. Secondly, seven performance property functions related to three state variables and three physical equations are identified and calibrated by simple temperature and power measurements. Finally, field experiments are conducted on a residential air conditioner to calibrate these performance property functions and validate the developed model. The validated results reveal the model can accurately predict the cooling capacity and electrical power input, with the normalized root mean square errors of 2.3% and 0.87% respectively. [ABSTRACT FROM AUTHOR]

• To reflect the impact of indoor units on system performance, a novel index and model were proposed. • Simulation and experimental results validated the proposed performance model. • Proposed index was applied in a field test in an office and a residential building. In the context of a large market share, enhancing the actual performance of variable refrigerant flow (VRF) air-conditioning systems is of considerable importance. The operating states of indoor units (IUs) significantly influence the energy efficiency of a VRF system with multiple indoor terminals. Therefore, it is challenging to address accurate and concise representation of the operational characteristics of IUs for analyzing their impact on system performance. This study explored a novel performance index called capacity utilization rate (CUR) to illustrate the effect of IUs on system performance and developed a performance model based on this index. Simulation and experimental results demonstrated that energy efficiency and evaporating temperature decrease with a reduction in CUR under specific ambient temperatures and part-load rates. The proposed performance model was applied to real buildings in field tests to examine the operational characteristics of the VRF system. This research offers valuable insights for system design, performance simulation, and performance evaluation of VRF systems. [ABSTRACT FROM AUTHOR]

• Propose a novel machine learning-based model predictive control (MLB-MPC) framework with predictive inputs of cooling load and outdoor wet-bulb temperature. • Obtain the optimal match of prediction models and optimization algorithms for improving the performance of the MLB-MPC. • Investigate the impact of disturbances on the performance of global optimization. • Achieve an energy-saving ratio of 7.1% for the air-conditioning system when compared with the measured operation data. The dynamic optimization of key setpoints (e.g., supply water temperatures of chillers and cooling towers, or indoor temperature and humidity) can track the efficient performance point of the air-conditioning system (ACS), thus obtaining a great energy-saving effect. The model predictive control is an effective way to precisely adjust these setpoints. However, most of the existing studies focused on simple linear strategies, e.g., experience-based, rule-based, or component-based, which results in unsatisfactory energy efficiency. Aiming at this, the paper proposed a novel machine learning-based model predictive control (MLB-MPC) framework with predictive inputs of disturbances. The framework first employed the grid search and cross-validation to optimize the total energy consumption prediction models (TECPMs), which were built by data-driven models (e.g., multiple linear regression, artificial neural network, support vector regression (SVR), and random forest). And then the optimization performances of each TECPM to the controlled variables were obtained by using different optimization algorithms (e.g., genetic algorithm, particle swarm optimization (PSO), and simulated annealing). Finally, the optimal match of TECPMs and optimization algorithms was achieved by trade-off among prediction accuracies, optimization accuracies, and optimization time. The case studies demonstrated that the optimal match for the MLB-MPC is SVR and PSO as they had the highest prediction accuracy (mean absolute percentage error of 2.5%) and shortest optimization time (41 ms/per) and optimization accuracies with little difference. After adopting the optimal match, the MLB-MPC with predictive disturbing inputs achieved a great energy-saving ratio of 7.1% for the ACS, which was only less than the MLB-MPC with ideal disturbing inputs by 0.4%. This indicated that exact predictions of disturbances (e.g., cooling load and outdoor wet-bulb temperature) are important for the MLB-MPC. The proposed MLB-MPC framework would provide a method for improving the energy efficiency of ACSs. [ABSTRACT FROM AUTHOR]

Energy consumption prediction is of great practical significance for the study of energy-saving scheduling of building air-conditioning systems. However, there are two problems in prediction models: (1) A single prediction model is difficult to solve the nonlinear problem of building energy consumption data. (2) A single prediction result can hardly reflect the uncertainty in the building operation process. Therefore, in this study, a hybrid prediction model with an improved whale optimization algorithm- optimization support vector machine (IWOA-SVM) is proposed based on the air conditioning system of a shopping mall in Xi'an. The WOA is improved using the opposition-based learning strategy (OBL), differential evolution algorithm (DE), and introducing a control parameter λ. The Confidence Interval (CI) of the model fitting error is used to represent the prediction results. The results show that the root mean square error (RMSE) of the optimized model is reduced by 9.5, and the (RMSE) of the total energy consumption prediction value after adding CI is >1.68. The method achieves higher model accuracy, reflecting the operational fluctuations of the building. [ABSTRACT FROM AUTHOR]

The real-time detection of indoor thermal comfort can bring significant benefits for energy-efficient controls over the heating, ventilation and air conditioning (HVAC) systems. At present, few studies have been conducted to propose non-intrusive and cost-effective solutions for the real-time sensing of individual thermal comfort. Leveraging advances in machine learning, this study proposes a data-driven method for real-time recognition of thermal comfort-related activities based on human inertial measurement unit (IMU) data. Using wearable devices at human wrists, experiments have been designed to collect prototype IMU data on 30 thermal comfort-related activities from building occupants. An end-to-end data analytic framework, which consists of offline training and online detection modules, has been developed for practical applications. Various feature engineering and state-of-the-art machine learning techniques have been implemented and tested to derive optimal data-driven solutions, leading to an activity recognition accuracy of 86.2%. The methods proposed provide an automated and feasible approach for thermal comfort-related activity recognition, which can be integrated with HVAC systems for smart and customized controls, such as personalized ventilation and air-conditioning. [ABSTRACT FROM AUTHOR]

Abstract Home energy management systems (HEMSs) are the system to manage the energy usage in houses. The use of HEMSs, and especially those which are capable of automatically controlling home energy appliances such as air-conditioners (ACs), is expected to manage energy utilized in domestic field effectively. In the present study, we focused on automatic AC operation by HEMS with the combined goal of improving thermal comfort while reducing electricity costs. In general, the room temperature and electricity consumption of an AC are highly dependent on the characteristics of the installation environment, so that the derivation of an appropriate AC operation plan is generally a difficult task. To tackle this problem, an energy management method to provide AC operation plan tailor-made for the target AC installation environmental by learning the characteristics of the installation environment (CIE) from the historical operation result data is proposed. The efficacy of the proposed method is verified via numerical and real-world experiments. [ABSTRACT FROM AUTHOR]

Abstract Understanding occupants’ thermal sensation and comfort is essential to defining the operational settings for Heating, Ventilation and Air Conditioning (HVAC) systems in buildings. Due to the continuous impact of human and environmental factors, occupants’ thermal sensation and comfort level can change over time. Thus, to dynamically control the environment, thermal comfort should be monitored in real time. This paper presents a novel non-intrusive infrared thermography framework to estimate an occupant's thermal comfort level by measuring skin temperature collected from different facial regions using low-cost thermal cameras. Unlike existing methods that rely on placing sensors directly on humans for skin temperature measurement, the proposed framework is able to detect the presence of occupants, extract facial regions, measure skin temperature features, and interpret thermal comfort conditions with minimal interruption of the building occupants. The method is validated by collecting thermal comfort data from a total of twelve subjects under cooling, heating and steady-state experiments. The results demonstrate that ears, nose and cheeks are most indicative of thermal comfort and the proposed framework can be used to assess occupants’ thermal comfort with an average accuracy of 85%. [ABSTRACT FROM AUTHOR]

Occupant behaviour on air conditioning directly influences building energy consumption. Current building energy simulation typically assumes that occupants operate air conditioners ideally ignoring the stochastic characteristic of occupant behaviour, which leads to a large deviation of predicted energy performance from real consumption. This paper proposed a methodology framework for modelling occupants’ stochastic behaviour on air conditioning. This method combined measured data, statistical analysis and logistic regression to derive stochastic behaviour models that give the probability of when and above which temperature occupants may turn on air conditioning, at what temperature they may set and when they may turn off air conditioning. The behaviour models developed in this paper were further incorporated in widely used building simulation engine—EnergyPlus. The accuracy of the developed simulation model was validated against field measurement. Using this model, the cooling energy performance was simulated and compared with results from building energy standard based settings. The result shows that simulation using settings according to building energy standard over-predicts cooling load and total cooling energy requirements by 113% and 5.6 times respectively, which are very large discrepancies and may lead to a misunderstanding of energy savings potential of energy efficiency measures. The methodology developed in this paper has generality and can be extended and applied in other buildings for constructing behaviour models of air conditioning. The limitations of the current research as well as future research works were also discussed. [ABSTRACT FROM AUTHOR]

The indoor environment is usually non-uniform and only local air zones need to be controlled. Although the specific calculation method of cooling load for the occupied zone has been proposed, no general definition and calculation method of cooling load exists for non-uniform indoor environment oriented to the local air zone. Thus, a general cooling load oriented to the local air zone, called local cooling load (LCL), was proposed for the design of air terminals in a general non-uniform indoor environment. Two calculation procedures of LCL, namely Procedure CAV (constant air volume) and Procedure VAV (variable air volume), were introduced. Experiments were conducted to illustrate LCL in a climate chamber measuring 4.12 m × 2.89 m × 4.2 m. Twenty cases were numerically investigated to analyze the influences of different ventilation systems, heat source locations, and target zones on the LCL. The results show that the two calculation procedures lead to almost the same values of LCL. The LCL can be less than, equal to, or even greater than the conventional cooling load in the uniform indoor environment, depending on the three influencing factors. The concept of LCL can be used for the air volume and temperature difference design of terminals in non-uniform indoor environments oriented to local requirements. [ABSTRACT FROM AUTHOR]

Hospitals present a widely recognized margin for energy conservation due to their elevated final energy use intensity. Specifically, operating rooms are among the functional areas with greatest energy demand in hospitals, given the stringent indoor conditions for patients and surgical staff. However, energy efficiency in surgical facilities has traditionally been overlooked by the scientific literature in favor of guaranteeing adequate air quality conditions. This paper assesses the potential energy savings that could be achieved in a surgical suite in a Spanish hospital by lowering the current ventilation rates to the lowest energy-demanding values recommended by the proper Spanish standard and other international codes. Likewise, the paper assesses the actual hygrothermal and ventilation conditions in the suite based on the results of a continuous monitoring campaign performed for a period of 12 days. Simulations undertaken with a dynamic energy model specifically developed and calibrated for this work revealed that the average energy intensity of the suite is 1021 kWh/m 2 /year (1685 kWh/m 2 /year for operating rooms), with 80% of the total annual thermal demand corresponding to periods of inactivity. In addition, energy savings between 40% and 80% are possible while complying with both national and international standards. [ABSTRACT FROM AUTHOR]

Coupling auxiliary cooling devices with ground source heat pump (GSHP) systems in cooling dominated areas can effectively solve the ground thermal imbalance problem. However, the input capacity of the auxiliary heat rejecter directly affects the performance of such hybrid ground source heat pump (HGSHP) systems. This paper presents an investigation on the optimal cooling tower input capacity of a cooling tower assisted GSHP system through both experiments and simulations. The experiments were carried out based on a HGSHP system implemented in an office building and the experimental results were used to validate a numerical model. A simulation system of the cooling tower assisted GSHP system was then developed using TRNSYS and validated against the experimental data collected. The impacts of the cooling tower input capacity on the soil temperature and the system performance were simulated. The results showed that the soil heat accumulation could be effectively alleviated when the cooling tower input capacity ratio (CTICR) was greater than 50%. The optimal cooling tower input capacity was highly dependent on the operation scenario used. The optimal CTICR under two operation scenarios considered was around 54%, while that under the other operation scenarios was around 63%. The control strategy based on the fixed temperature difference between the cooling water leaving the heat pump and the ambient air dry-bulb temperature was found to be the optimal control strategy for the system studied. [ABSTRACT FROM AUTHOR]

Energy conservation in residential buildings has gained increased attention due to its large portion of global energy use and potential of energy savings. Occupant behavior has been recognized as a key factor influencing the energy use and load diversity in buildings, therefore more realistic and accurate air-conditioning (AC) operating schedules are imperative for load estimation in equipment design and operation optimization. With the development of sensor technology, it became easier to access an increasing amount of heating/cooling data from thermal energy metering systems in residential buildings, which provides another possible way to understand building energy usage and occupant behaviors. However, except for cooling energy consumption benchmarking, there currently lacks effective and easy approaches to analyze AC usage and provide actionable insights for occupants. To fill this gap, this study proposes clustering analysis to identify AC use patterns of residential buildings, and develops new key performance indicators (KPIs) and data analytics to explore the AC operation characteristics using the long-term metered cooling energy use data, which is of great importance for inhabitants to understand their thermal energy use and save energy cost through adjustment of their AC use behavior. We demonstrate the proposed approaches in a residential district comprising 300 apartments, located in Zhengzhou, China. Main outcomes include: Representative AC use patterns are developed for three room types of residential buildings in the cold climate zone of China, which can be used as more realistic AC schedules to improve accuracy of energy simulation; Distributions of KPIs on household cooling energy usage are established, which can be used for household AC use intensity benchmarking and performance diagnoses. [ABSTRACT FROM AUTHOR]