Temperature Set Point Research Articles

Demand response flexibility with synergies on passive PCM walls, BIPVs, and active air-conditioning system in a subtropical climate

Energy-efficient buildings and renewable power supply are two mainstreams for achieving zero-energy buildings and contributing to the energy paradigm transition towards carbon neutrality from building sectors. Due to the significant heat loss and large surface areas, building envelopes are full of potentials in reducing the cooling/heating load through enhanced thermal insulation performance and converting solar radiation into renewable energy with photovoltaics (PVs). However, smart connection with synergic complementary functions has not been comprehensively studied among heating, ventilation, and air-conditioning (HVAC) systems, phase change material (PCM) wall, and building integrated PVs (BIPVs). In this study, a transient and dynamic platform for energy-efficient buildings was established, integrating PCM walls and BIPVs. Multiple functions of the platform can be achieved, e.g., enhancement in thermal inertia and storage capacity of building envelopes with PCM, solar shading from BIPVs, renewable energy-driven HVAC, thermal regeneration on PCM with nighttime cooling and pre-cooling in the built environment. Synergistic functions between BIPVs and passive PCM layer are studied, e.g., the BIPVs can provide solar shading and thermally regenerate the PCM to resist the exterior heat flux during 1st-7th Aug. In-depth analysis was conducted on thermodynamics of PCMs, in respect to different geometrical (e.g., PCM thickness) and thermophysical parameters (e.g., latent heat and thermal conductivity on PCM). Afterwards, flexible charging/discharging strategies on latent heat with smart control on an indoor setpoint of HVAC were proposed, implemented, and compared, so as to shift grid electricity from off-peak to peak period. The proposed PCM charging/discharging strategies include pre-cooling for load shifting and floating indoor air setpoint. Parametrical analysis on different off-peak electric prices was conducted, to provide qualitative results on annual import cost savings. Research results indicate that, due to the shading of solar radiation, the vertical BIPVs will reduce the building cooling load from 73.5 to 67.9 kWh/m2·a by 7.6%, and the peak power from 503.1 to 466.8 kW by 7.2%. Synergistic functions between BIPVs and passive PCM walls can resist the heat flux with a higher magnitude for a shorter period during 1st-7th Aug, and provide heat with a higher magnitude for a longer period during 1st-7th Jan. Furthermore, by adopting the pre-cooling strategy with smart operation on HVAC systems for PCM regeneration, the PCM melting fraction range can be expanded from [0.92, 1] to [0.75, 1], together with the increase in accumulated heat from 307427.8 to 337881.1 kJ by 9.9%. By adopting the floating indoor air setpoint temperature strategy with smart operation on HVAC systems, the annual import cost is decreased from 65.1 to 61.1 HK$/m2·a by 6.1%. Economic performance analysis indicates that, with the grid feed-in tariff in Hong Kong, the simple payback time (SPBT) is around 17 years, while the PCM layer will prolong the SPBT to exceed 20 years. This study can enhance the thermal regulation capacity of building envelopes, together with a flexible connection between active HAVC systems and passive PCM walls with techno-economic viability.

Grey-box modelling and virtual sensors enabling continuous commissioning of hydronic floor heating

Operating district heating systems with low supply and return temperatures improves heat production and distribution efficiency, permitting greater integration of renewable heat sources. Low-temperature district heating is viable without compromising comfort, but faults in end-users’ heating systems constrain temperature reductions. Such faults include malfunctioning valves, improper hydronic balancing, and excessive supply temperature setpoints. Occupants lack the resources to detect and diagnose these faults, so there is a need for automated solutions without requiring additional hardware. This paper proposes a method for improving the operation of an apartment's hydronic floor heating system using data from room thermostats, a heat meter and a circulation pump to identify a grey-box model of the system. The resulting model virtually senses each room loop's heat flux, flow, return temperature, and flow coefficient. The authors tested the model on a low-energy apartment in Denmark, using it to diagnose causes of high return temperatures, including poor hydronic balancing and an excessive supply temperature setpoint and pump setting. The authors also used the model to predict the minimum permissible supply temperature maintaining comfort, yielding a reduction in the energy-weighted supply and return temperatures of 8.6 °C and 6.5 °C, respectively.

Fundamental study on oxidation properties at elevated pressure of typical renewable synthetic liquid fuels through low-temperature CO2 electroreduction

Using renewable energy to convert CO2 into liquid fuels is a game changing technology that makes transportation and industrial fuels independent of fossil energy. Electrochemical reduction of CO2 is such an example. Liquid fuels produced through this route can be potential candidates for internal combustion engine in the future. However, organic acids such as acetic acid and formic acid will be formed during this process inevitably. To understand oxidation properties of these renewable synthetic fuels, a newly developed variable pressure laminar flow reactor has been used for experimental investigations, and methanol experiments have been performed to validate the reliability of this platform. Pure ethanol and binary fuel experiments have been carried out at 700–1000 K, 10 bar and Φ = 0.25. Several kinetic models obtained from literature has been validated against experimental data and further used for kinetic analysis. Through comparison of experimental results, formation rates of intermediates such as hydrogen, carbon monoxide, methane and acetaldehyde at lower temperature setpoints are larger in pure ethanol experiments than that in binary fuel. This is because acetic acid consumes reactive radicals and then produce less reactive methyl radical, which directly competes with H-abstraction reactions of ethanol. Furthermore, the H-abstraction reaction products of acetic acid will consume methyl radical, this chain-terminating reaction in addition to the consumption of reactive radicals reduce the low-temperature reactivity of the whole system. At higher temperature, stable species formed by acetic acid will be consumed, which makes the concentration of four aforementioned intermediates higher in binary fuel experiments at intermediate to high temperature region. Present work will be conductive to understanding direct usage of renewable synthetic liquid fuels in engine. Further investigation of synthetic fuels can be proceeded based on results of present work.

Embedded Real-Time Clothing Classifier Using One-Stage Methods for Saving Energy in Thermostats

Energy-saving is a mandatory research topic since the growing population demands additional energy yearly. Moreover, climate change requires more attention to reduce the impact of generating more CO2. As a result, some new research areas need to be explored to create innovative energy-saving alternatives in electrical devices that have high energy consumption. One research area of interest is the computer visual classification for reducing energy consumption and keeping thermal comfort in thermostats. Usually, connected thermostats obrtain information from sensors for detecting persons and scheduling autonomous operations to save energy. However, there is a lack of knowledge of how computer vision can be deployed in embedded digital systems to analyze clothing insulation in connected thermostats to reduce energy consumption and keep thermal comfort. The clothing classification algorithm embedded in a digital system for saving energy could be a companion device in connected thermostats to obtain the clothing insulation. Currently, there is no connected thermostat in the market using complementary computer visual classification systems to analyze the clothing insulation factor. Hence, this proposal aims to develop and evaluate an embedded real-time clothing classifier that could help to improve the efficiency of heating and ventilation air conditioning systems in homes or buildings. This paper compares six different one-stage object detection and classification algorithms trained with a small custom dataset in two embedded systems and a personal computer to compare the models. In addition, the paper describes how the classifier could interact with the thermostat to tune the temperature set point to save energy and keep thermal comfort. The results confirm that the proposed real-time clothing classifier could be implemented as a companion device in connected thermostats to provide additional information to end-users about making decisions on saving energy.

Human-in-the-loop methods for occupant-centric building design and operation

A comfortable indoor environment should be one of the main services buildings provide. However, technical building systems are typically designed and operated according to fixed set-point temperatures determined by the ‘one-size-fits-all’ principle assuming universal thermal comfort requirements, which has been questioned in the last fifty years. Designing and implementing comfortable set-point modulations that consider occupant feedback would be beneficial in terms of increasing comfort, potentially reduce energy consumption and significantly support the clean energy transition. An exploratory study aimed at predicting the thermal preferences of human subjects exposed to a dynamic thermal environment is presented. Using data acquired from a laboratory experiment where subjects were exposed to precisely controlled thermal ramps in an ‘office-like’ climatic chamber, cluster-specific and population-averaged methods are designed to handle the group-level residual during the prediction of the thermal preference votes. The results show that both approaches are valid strategies for modelling thermal preference votes and are effective in supporting a concrete occupant-centric building design and the building’s operation. Furthermore, the population-averaged approach is suitable for the occupant-centric building design phase, where the target is an ‘average’ occupant. The cluster-specific method is best suited to meet the needs of a specific occupant and is suitable for implementation in the operational phase of the building.

Impacts of phase change materials on performance of operational peak load shifting strategies in a sample building in hot‐arid climate

AbstractBuildings consume around 32% of the produced energy, worldwide and around 19% of the greenhouse gas emissions are related to them. It makes the buildings one of the largest energy consumers and one of the major sources of greenhouse gases emission. HVAC systems are one of the largest consumers of energy in buildings. In hot arid climates, cooling systems used in buildings impose a large pressure on the power grid, which may lead into blackouts. In order to address this problem, several approaches have been examined. Thermal energy storage, in particular, latent heat storage using phase change materials (PCM) has been notably considered in construction applications because of high energy storage densities. In this paper, the effect of using PCMs on energy consumption and peak load demand of a sample building is studied. Besides, impacts of precooling as a peak load shifting strategy is also investigated in presence of PCMs on the energy consumption of the sample building. The study was carried out for the weather condition of Kerman, Iran and the simulation is performed by TRNSYS software. Three types of phase change materials were investigated for two different scenarios. The simulation was performed at two stages and two modes for each stage which each mode had its own set point temperature. The application of PCMs was considered at the first stage and precooling added to the simulation at the second stage. The results of the first stage showed that employing PCMs in the studied building reduces the cooling load up to 13%. With precooling, the cooling load reduction was up to 16%. At all stages peak loads decreased with maximum 14% at the first stage and 42.5% at the second stage. It has been showed that the solid–liquid phase change temperature of the PCMs close to the selected room temperature, have significant impact on the performance of PCMs for reducing the energy consumption of the sample building compared to the other thermal properties. Due to the low electricity prices in Iran, despite the high amounts in load reduction achieved by each strategy, the cost savings during hot months of the year was not significant.

Thermal performance assessment of phase change material for electronic cooling: An experimental investigation

AbstractIn the present study, an experimental investigation is performed to check the performance of phase change material (PCM) OM42 for passive electronic cooling applications. Rectangular fins were used as PCM thermal conductivity enhancers (TCEs) to enhance the thermal conductivity of PCM. The experiments were performed (a) by maintaining the required input heat flux to the heater and (b) by maintaining the set point temperature (SPT) of the heater. The heater wattage controller was used to supply the required power to the heater and thermostat W1209 was used to control the SPT of the heater. The experiments were performed in the combination of PCM and fins, with fins only, With PCM only, and with PCM and fins. The obtained results signify improved performance of the heat sink when PCM with fins was used. At input heat flux of 1, 2, 3, 4 and 5 kW/m2 the heat sink reaches the critical temperature in 80, 60, 55, 50 and 40 minutes respectively in the absence of PCM without fins, but with the presence of PCM without fins and PCM with fins, the heater sink is well below the critical temperature for a complete charging cycle. At input heat flux of 4 and 5 kW/m2, the intensity of PCM melting rate is faster and hence solid‐liquid interface decreases quite rapidly as the buoyancy force dominates the viscous force since the volume of liquid PCM rises as the natural convection effect increases in the liquid region.

Stochastic modelling of flexible load characteristics of split-type air conditioners using grey-box modelling and random forest method

Split-type air conditioners (ACs) are the major contributors to the total electricity use and peak power demand in residential buildings due to their high penetration. Accurate prediction of their energy consumption plays a significant role in demand side management (DSM), because it can help exploit the demand response (DR) potential of these ACs fully. However, the existing studies on the load characteristics modelling of split-type ACs are still not comprehensive, especially in the consideration of occupant behavior and appliance characteristics. To solve this problem, this study has developed a novel flexible load characteristics model by combining the stochastic method, grey-box modelling (i.e., equivalent thermal parameter (ETP) model) and random forest method. The stochastic method can help capture the dynamic occupants’ energy-related behaviors considering various family structures. Random forest can address the difficulties in simulating the power characteristics of variable-speed ACs under various operating conditions. With the prediction error of 2.8%, the proposed model can effectively characterize the energy performance of split-type ACs at both individual and aggregate levels. To better show the load flexibility of ACs, an investigation on DR potential of split-type ACs was also carried out by resetting temperature setpoints. The result showed that the maximum DR potentials at the scope of 1000 aggregated households are 394.54 kWh and 323.82 kWh on weekdays and weekends, respectively. The developed flexible load characteristics model of ACs can be used by utility companies for DR potential assessment and model-based DR control for a single household or building clusters.

Using neural network in a model-based predictive control loop to enhance energy performance of buildings

Today’s environmental, energy and economic challenges require changes in the control strategies of HVACs’ systems, since they account for more than 60% of the building energy consumption. An optimal control law should be applied to reduce this consumption. To achieve this goal, a detailed description of the building is required, its construction components as well as the description of the occupants’ activities. A good knowledge of all building components is essential, especially the thermal characteristics of the building envelope, to build an energy model of the building. The dynamic thermal simulation tool, as EnergyPlus in this case, allows us to set up a huge database of the thermal behavior of our architectural model. This database was used for the training of an Artificial Neural Network model for modeling the thermal behavior of the building, with the aim of controlling the thermal comfort of the occupants, which means maintaining the temperature of the room within a setpoint temperature range. The developed control method, introduced in this work, reduced the energy consumption for cooling and heating, respectively, from 11.834 kWh to 9.025 kWh (27.34%) and from 4.631 kWh to 2.824 kWh (39.02%), compared to the On/Off control, during the 1 day of simulation.

Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm

Heating, ventilation, and air-conditioning systems provide a comfortable indoor thermal environment, but high energy consumption is often necessary to achieve an adequate level of indoor thermal comfort. However, it is challenging to design an energy-efficient thermal comfort control strategy, mainly because the internal thermal environment is influenced by complicated factors and difficult to model accurately. To solve this problem, a control strategy incorporating the parallel temporal convolutional neural network (PTCN) and the improved chimp optimization algorithm (ICHOA) is proposed for thermal comfort control of buildings. Thermal comfort control is transformed into a cost-minimization problem by establishing an objective function for both the future thermal comfort of the occupants and energy consumption and optimizing multiple air-conditioning temperature set points for the coming day. First, to ensure the prediction performance, a PTCN model was developed to predict the energy consumption and thermal comfort under different factors. An opposition-learning-based adaptive chimp algorithm was then used to solve the objective function to output the optimal set temperature. Finally, the superiority of the PTCN-ICHOA optimization strategy was verified using an office building in Jinan as an example. In winter and summer experiments, the proposed PTCN model achieved the lowest prediction errors among the models compared in terms of energy and temperature prediction. Furthermore, the PTCN-ICHOA optimization model exhibited faster convergence than the other models for both experiments. Through the proposed optimization strategy, energy consumption savings of approximately 6.3%–8.1% can be achieved while maintaining indoor thermal comfort.

Improving residential building energy simulations through occupancy data derived from commercial off-the-shelf Wi-Fi sensing technology

One of the main required actions for moving forward in zero-carbon cities’ mission is to increase building energy efficiency. Building energy simulations are key tools for HVAC control, building energy management, testing different building energy efficiency scenarios, and providing feedback to the occupants. Unfortunately, these tools have a significant weakness in occupant behavior modeling and prediction. Several recent studies used different occupancy measurements and methods to predict occupant behavior. However, they suffer from threatening the occupant's privacy and the high cost of their infrastructure and deployment. This paper aims to leverage passive WiFi sensing methods for occupant behavior estimation using commercial off-the-shelf WiFi devices, for which the infrastructure is already available in many buildings. Passive and device-free WiFi sensing does not threaten the occupant's privacy, and its deployment cost is low. Therefore, this method of occupancy measurement has the potential to be used on an urban scale and to play an essential role in zero carbon and smart cities in the future.Different machine learning algorithms have been applied to the collected, denoised, and preprocessed Channel State Information (CSI) data to estimate the occupant behavior. Five schedules of occupancy, occupant activity, lighting, electrical equipment usage, and thermostat set point temperature could be extracted and estimated from CSI data. In the last step, all estimated schedules were fed into EnergyPlus, an energy simulation software, along with other required data for accurate energy demand calculation to estimate heating and cooling demand, which can be used for HVAC control and occupancy feedback.

Thermal performance enhancement of a new dual-PCM heat sink using two-objective optimization

Phase change materials (PCMs) can effectively improve the performance of electronic heat sinks especially when subjected to intermittent heat loads. PCMs however brings some shortcomings such as increased size of component, and slowed heat rejection during the discharging, which urged the inevitability in development of optimized design. This study presents the results of the two-objective shape optimization of a new dual-PCM heat sink. The cooling phenomenon is analysed with thorough and realistic consideration of heat loss through the fin tips to the ambient, as well as heat transfer between the PCM and the air above it. Natural convection of liquid PCM and its volume variation during phase change transition are taken into account. Two-objective optimizations are performed to maximize the heat sink operational time in the charging process and minimize the discharging time, for three critical temperatures of 50 °C, 65°C, and 80 °C. Response Surface Methodology (RSM) is utilized to attain this goal. The fin thickness, the fin height, the width of ducts and the base thickness are selected as design parameters. The optimum volumetric percentages of PCM are found to be 80.50%, 76.94% and 74.81% for set point temperatures (SPTs) of 50 °C, 65 °C, and 80 °C, respectively. The optimized designs for three SPTs deliver charging periods up to 7% longer and discharging periods up to 4.2% shorter than the reference geometry. Furthermore, total volume of the heat sink is decreased up to 23% due to current optimizations, signifying a favourable compact design.

Devulcanization of natural rubber/styrene-butadiene rubber unfilled blend in supercritical carbon dioxide using Tulbaghia violacea crude extract

A non-toxic and readily available potential devulcanizing agent, viz. Tulbaghia violacea Harv. plant bulbs crude extract , was used for the first time to non-isothermally devulcanize an unfilled natural rubber/styrene butadiene rubber blend in supercritical carbon dioxide medium. Temperature set-point and pressure were kept at 120 °C and 8 MPa, respectively, while the amount of extract and reaction time were optimized. Based on simultaneous optimization of the amount of extract and the initial time of devulcanization, the reactivity of the organo-sulphur compounds in an extract with the vulcanizate is seemingly dependent on the reaction time and temperature. Though organo-sulphur compounds in the plant bulbs crude extract had a significant effect on the vulcanizate, heat alone also caused chemical bonds breakage . Treatment with plant bulbs crude extract decreased the crosslink density while increasing both the sol fraction % and percent devulcanization to 45% and 75%, respectively, when devulcanization was performed for 6–17 min. Organo-sulphur compounds in plant bulbs crude extract had no effect when the reaction time was 18 min or more. Swelling tests, as well as gel permeation chromatography , Fourier-transform infrared spectroscopy and thermogravimetric analyses, on extract-treated and untreated vulcanizate suggested that the Tulbaghia violacea plant bulbs crude extract has the potential of producing improved-quality recovered rubber.

Technical and economic analysis of the provision of ancillary services through the flexibility of HVAC system in shopping centers

To address the increasing demand for ancillary services, new demand response strategies (DR) are being developed that involve the building sector to provide flexibility to the transmission system operator (TSO). In this respect, commercial buildings have great potential to become key players in providing flexibility to the TSO by changing their load profiles for heating, ventilation and air conditioning. This study presents a case study of a Mediterranean shopping centre heated and cooled by a heat pump and participating in the ancillary services market according to the specific rules of the Italian pilot project UVAM. The analyses start from the daily consumption profile assuming conventional operation for the HVAC system (baseline) during cooling and heating periods. Then two DR strategies are investigated, the first of which is to change the indoor set point temperature and the second is to reduce the electricity power to the HVAC systems, without introducing pre-cooling or pre-heating periods. The results show that the studied flexibility strategies allow to obtain, for each DR event, over 60 kW of downward flexibility power and a reduction of energy consumption between 128 and 160 kWh during summer, and over 50 kW of downward flexibility power and a reduction of energy consumption between 40 and 160 kWh. This study identifies opportunities and barriers for commercial buildings to participate in the ancillary services market. However, the proposed DR strategies may lead to an unacceptable percentage of dissatisfaction (PPD), especially during the cooling season.

Lightweight methane-air gas turbine controller and simulator

• Lightweight CH4/air gas turbine simulator. • System identification, cycle, and sensitivity analyses CH4/air gas turbines. • Developing and calibrating a CH4/air gas turbine controller. • The potential of utilizing waste heat utilization mechanisms. • Control measures to eliminate turbine overheating. Due to the uncertainties of determining stable, efficient, and safe operating conditions of methane-air gas turbines and the complexity of involving multivariable parameters, the task of controlling and analyzing methane-gas turbines remains a challenge despite the existing methods of control and analyses in the literature. In fact, as far as the methane-air gas turbines are concerned, there is still much room for improvement within the criterion of (computation effort, settle time, its capacity to adjust with a variable range of set-points, as well as its stable, steady, and dynamic outputs can all be enhanced). Furthermore, an accurate system-identification phase has been adopted in this paper to initiate the process of advancing an efficient gas turbine controller by determining the system's sensitivities towards the involved multivariable parameters. Therefore, this paper presents a control and analysis tool for methane-air gas turbines. This tool is a ‘light code (the time to reach the desired set-point (i.e., settle period reaches 1 s)) to serve as a methane-air gas turbine controller that can be used for experimentation or applied on an industrial scale. Moreover, the code is designed with a user interface that enables the simulations and system identification for the methane-fueled gas turbine, thus; ensuring that the governing parameters are calibrated precisely. The code was created using LabVIEW and GASEQ and is driven by conventional gas turbine cycle assessments. A simulation case study has been utilized as a system identification phase to determine the system's sensitivity of temperatures and works at the three primary stages of a simple cycle methane-air gas turbine (compressor, combustion chamber, and turbine) toward the multivariable input parameters of the compression stage (compressor pressure ratio, efficiency, inlet pressure, and temperature), the combustion phase (methane, airflow rates) and the expansion phase (turbine ratio of pressures and isentropic efficiency). Based on the sensitivity analysis performed through the developed code, the turbine output temperature is shown to be very sensitive to the turbine's parameters ( P 04 / P 03 and ε t ). This essentially emphasizes the possibility of including waste heat utilization mechanisms (i.e., heat exchangers, combined cycles) for turbines with high P 04 / P 03 or/and low ε t , resulting in a high turbine outlet temperature. Based on the performed system identification phase and sensitivity analyses, the intervals of the Proportional controller gain ( K c ) , Integral time ( t i ) , and Derivative time ( t d ) have been identified to guarantee a settling period ( T s ) within [1 s-60 s] to achieve the desired temperature set-point which eliminates turbine overheating.

Indoor Thermal Environment and Energy Characteristics with Varying Cooling System Capacity and Restart Time

Office cooling systems are controlled with on/off control according to typical occupancy patterns. During unoccupancy, the cooling systems remain switched off to reduce unnecessary energy consumption. During occupancy, however, the cooling systems are in operation to decrease the indoor air temperature, which is increased during unoccupancy, to the cooling set-point temperature. The time required to decrease the indoor air temperature to the cooling set-point temperature is defined as the “recovery time”. According to the recovery time, the indoor thermal comfort at the occupancy start time may worsen, and unnecessary energy may be consumed. Moreover, a cooling system capacity affects the recovery time and the energy consumption because the amount of heat that the cooling system can remove varies according to its size. Therefore, it is necessary to analyze the indoor thermal environment and the energy consumption according to the capacity and the restart time of the cooling system. This study implemented a building system energy simulation using EnergyPlus to evaluate the indoor air temperature, recovery time, and energy consumption of the cooling system while varying the capacity and restart time. As a result, the recovery time was between 49 and 425 min. and energy consumption varied between 419.0 and 521.4 kWh for various capacities. The recovery time was between 26 and 153 min. and energy consumption was between 426.0 and 439.0 kWh for various restart times.

Optimization of energy use and academic performance for educational environments in hot-humid climates

Thermal comfort and air quality are important factors for indoor environment in school buildings owing to the strong relationship with students’ academic performance. However, the expense of achieving expected environment quality in classrooms is unaffordable by many schools; thus, this study aims to suggest optimal strategies for balancing energy use and indoor environment quality simultaneously. Through the field measurement and building energy simulation, we analyzed the influences of temperature set-point and ventilation rate on environment quality and clarified the optimal trade-off measures for Taiwanese primary school. The optimal combination of set-point and ventilation rate considered academic performance and cooling energy based on single-objective optimization is proposed and revealed the highest hourly average cost performance is 2.13%/kWh each hour in Taipei and 2.03%/kWh each hour in Kaohsiung, which respectively corresponding to the set-point and ventilation rate of 24 °C and 26 °C, 12.6 L/s-person and 22.05 L/s-person. Moreover, the optimal combinations concerning financial constraints are also confirmed by Pareto front through multi-objective optimization. The results indicated that to achieve the academic performance higher than 85% in primary school, the optimal solution sets are scattered between the set-point of 23 °C–25 °C and the ventilation rate of 20 L/s-persoñ25.2 L/s-person. Consequently, this study provides recommendations for policymakers to formulate strategies to maximize the academic performance in hot-humid climate zone.

Investigation on typical occupant behavior in air-conditioned office buildings for South China’s Pearl River Delta

The excessive simplification of occupant behavior is considered as the most important factor that affects the uncertainty of building performance simulation, thus affects the reliability and generalizability of simulation-based design and forecast. In this paper, occupant behavior in air-conditioned office buildings of the Pearl River Delta (PRD) region was investigated and defined. Copies of 873 questionnaires about the occupant behavior in air-conditioned office buildings in the PRD region were collected to study the relationship between indoor environment quality and adaptive behaviors. Eight typical office occupant schedules were defined via K-means clustering method. A probability prediction model of cooling temperature set-point was established by using the Ordinal Logistic Regression method. According to the different control modes of air conditioning, window, blind and lighting equipment, four types of typical behavior patterns were proposed using the K-prototype clustering method, which could be developed into 20 typical occupant behavior styles of office buildings in the PRD region.

Thermal mass impact on energy consumption for buildings in hot climates: A novel finite element modelling study comparing building constructions for arid climates in Saudi Arabia

Contradictory conclusions have been reported on whether thermal mass reduces or increases energy demand in hot and cold climates. A novel approach to measuring the impact of thermal mass on the building’s energy consumption is presented. The method aims to better represent reality and applies two different control conditions simultaneously in a numerical model: the first condition, which has been used in past studies, adjusts the internal boundary condition based on the time duration of heating/cooling activation (simulating an occupancy pattern) and the second, which has not been used in past studies, adjusts the internal boundary condition based on both the time duration and the internal temperature (simulating an occupancy pattern and the thermostatic control system). When investigating the impact of thermal mass on the internal temperature in a scenario where no heat is transferred into the building, the high thermal mass outperforms the low thermal mass, showing a time-shifting effect, long time lag, and small decrement factor. However, when modelled using the method presented in this paper, high thermal mass results in greater energy consumption during summer and winter periods in Saudi Arabia. During transition seasons where temperature swings occur on either side of the indoor set-point temperatures, thermal mass reduces thermal transmission. The impact of thermal mass is also investigated for several climatic conditions in Saudi Arabia. Simulation results show that an internally exposed thermal massive wall section results in greater energy consumption when compared to a similar building with isolated thermal mass (i.e. insulated internally). This is due to the fact that even though the internal temperature inside the building is being cooled/warmed up to the set-point, the thermal mass continues to transfer heat that is absorbed by the building’s fabric from/to the interior, placing a greater demand on the cooling/heating system. Over the course of a year, however, the impact of thermal mass is negligible when compared to other factors, such as the insulative properties of the building’s fabric.

Occupant-centered evaluation on indoor environments and energy savings of radiant cooling systems with high-intensity solar radiation

Solar radiation has an overwhelming influence on indoor thermal and light environments, as well as building energy consumption. In this study, radiant cooling systems were applied to reduce the negative effects of solar radiation. The tests were carried out with the floor radiant cooling system (FRC), ceiling radiant cooling system (CRC), ·and conventional air conditioning system (AC), under typical warm days. An occupant-centered body model was presented to comprehensively evaluate the non-uniform thermal environment caused by solar heat. Besides, the effects on the cooling performance of cooling systems were evaluated by simulation and experiments. The results demonstrate that the cooling capacity rapidly increased with the intensity of the solar radiation. During beam-exposed period, cooling capacity of FRC system increased from 55.6 W/m2 to 210.0 W/m2 and the proportion of shortwave radiant heat flux qsr reached 84%. Furthermore, solar radiation also deteriorated the non-uniform thermal environment. The FRC system could reduce the mean radiant temperature by the maximum of 8.0 °C. Exposed to the beam solar radiation was the main reason that caused large vertical stratification of temperature and dissatisfaction. The FRC system effectively performed better with the maximum percentage dissatisfied value reduced by 14.76 %. Finally, the energy-saving potential of RC systems was analyzed. After the set-point temperature was corrected, the FRC system reduced the typical day’s energy consumption by 21.7% and annual energy consumption by 12.2%. Appropriate adoption of RC systems is an energy-saving method to improve thermal comfort of indoor environments with high-intensity solar radiation.