Predicted Mean Vote Index Research Articles

Heat stress, thermal comfort and control strategy in a warm-humid workplace.

The purpose of this study was to design a local ventilation system (LVS) to help reduce the moisture content of a Scalder hall, evaluate its comfort and thermal stress before and after implementation of LVS and introduce an appropriate index to evaluate warm and humid workplaces. The design of the LVS was performed according to the ACGIH standard (VS-30-01). Heat stress and thermal comfort assessment were performed before and after LVS using humidity index (Humidex), discomfort index (DI), heat index (HI), wet-bulb globe temperature (WBGT) and predicted mean vote index (PMV) indices and the results were compared with predicted mean vote index-predicted percentage of dissatisfied (PMV-PPD) subjective indices. The results of heat stress parameters showed that LVS was able to reduce relative humidity (RH) and wet temperature (tnw) by 47% and 7 ° C, respectively. This has caused subjects to feel the heat from hot and very RH hot to warm and the hot and percentage of dissatisfaction has dropped by more than 70%. Design and implementation of a LVS reduced the ambient tnw by decreasing RH. Results also showed in warm and humid workplaces, DI index are highly correlated with subjective evaluation of thermal comfort and this index can be used to evaluate the thermal conditions of the workplaces.

Modelling of Indoor Air Quality and Thermal Comfort in Passive Buildings Subjected to External Warm Climate Conditions

Air renewal rate is an important parameter for both indoor air quality and thermal comfort. However, to improve indoor thermal comfort, the air renewal rate to be used, in general, will depend on the outdoor air temperature values. This article presents the modelling of indoor air quality and thermal comfort for occupants of a passive building subject to a climate with warm conditions. The ventilation and shading strategies implemented for the interior spaces are then considered, as well as the use of an underground space for storing cooled air. The indoor air quality is evaluated using the carbon dioxide concentration, and thermal comfort is evaluated using the Predicted Mean Vote index. The geometry of the passive building, with complex topology, is generated using a numerical model. The simulation is performed by Building Thermal Response software, considering the building’s geometry and materials, ventilation, and occupancy, among others. The building studied is a circular auditorium. The auditorium is divided into four semi-circular auditoriums and a central circular space, with vertical glazed windows and horizontal shading devices on its entire outer surface. Typical summer conditions existing in a Mediterranean-type environment were considered. In this work, two cases were simulated: in Case 1, the occupation is verified in the central space and the four semi-circular auditoriums and all spaces are considered as one; in Case 2, the occupation is verified only in each semi-circular auditorium and each one works independently. For both cases, three strategies were applied: A, without shading and geothermal devices; B, with a geothermal device and without a shading device; and C, with both shading and geothermal devices. The airflow rate contributes to improving indoor air quality throughout the day and thermal comfort for occupants, especially in the morning. The geothermal and shading devices improve the thermal comfort level, mainly in the afternoon.

Evaluating Thermal Comfort and Overheating Risks in A Social Housing Prototype: As Built Versus Retrofit Scenarios

Climate change has highlighted the importance of thermal comfort and its health-related outcomes, particularly for the most vulnerable members of society living in social housing. Due to their vulnerable living conditions, low-income people are more exposed to negative outcomes of overheating and cold indoor temperatures in buildings. Previous studies suggest that there is a significant risk of overheating in retrofitted buildings both for the current and future weather scenarios. The UK government has introduced new building regulations to assess and limit the risk of overheating in new buildings; however, there is still a need to assess and improve conditions for existing and retrofitted properties. This study aims to evaluate the effect of retrofit strategies on thermal comfort and the risk of overheating in social housing under current and future climatic conditions. A typical case study building was simulated in DesignBuilder to assess thermal comfort conditions for upgraded building fabric to Part L of the UK building regulations and Passive House standards. The summer results were analyzed according to CIBSE TM59 while the Predicted Mean Vote index (PMV) was used for winter analysis. Findings revealed that the south-facing bedrooms are most exposed to overheating. Risk of overheating significantly increased for the future weather scenarios by up to 10 times while winter thermal comfort improved for the retrofitted scenarios.

Improving the thermal comfort model for students in naturally ventilated schools: Insights from a holistic study in the Mediterranean climate

The thermal comfort models defined in existing standards are usually ineffective in predicting students' mean thermal sensation. Accordingly, overheating and overcooling are frequently reported, which affect students’ well-being and cause unnecessary energy waste. Existing studies have not further explored the model improvement strategies for educational buildings. Hence, this research aims to improve the mean thermal sensation prediction model for students in naturally ventilated schools by incorporating correlated factors, based on a holistic field survey in the Mediterranean climate. The results indicate that the modified extended predicted mean vote of thermal adaptations reinforced around thermal neutrality has the highest accuracy among all validated adaptive predicted mean vote models. However, the performance of the adaptive predicted mean vote models is limited by the deficiencies of the original predicted mean vote model. The overall performance of the improved models was doubled after introducing the identified correlated factors, but the intrinsic error of the predicted mean vote index can slightly affect the models. As a result, the improved model built directly with the identified correlated factors achieved the best performance. Moreover, the potential of the machine learning models is limited by the volume of the mean thermal sensation vote data that can often be obtained, therefore conventional models such as multiple linear regression can be considered an effective and practical tool for developing the mean thermal sensation vote models. The findings of this study benefit the well-being of students and the energy conservation of educational buildings.

Data-driven flexibility evaluation methodology for community integrated energy system in uncertain environments

Flexibility operation and evaluation have become central and critical for grid integration of high percentage of renewable energy generations (REGs). However, the interactions between multiple energy forms in community integrated energy system (CIES) and the uncertainty of REG significantly complicate the quantification of flexibility. To this end, a novel two-stage data-driven flexibility evaluation methodology is proposed for CIES. The first stage performs optimal scheduling of multiple flexible resources, where the flexible thermal comfort requirements of users in integrated response demand are considered by introducing a predicted mean vote index. The second stage refines the flexibility supply model for flexible resources, and carves out the system flexibility supply domain by enveloping the net load. To overcome the uncertainty of REG, a data-driven scenario analysis approach is developed, which fully captures the renewable output characteristics under different climate environments and avoids complicated explicit feature model construction. Furthermore, a set of evaluation metrics is proposed from the aspect of direction, amount, and frequency to quantify the flexibility. The effectiveness and accuracy of the proposed methodology are verified by applying it to a modified CIES located in North China. The sensitivity analysis of multiple time scales and flexible resources to operational flexibility and economy of CIES is presented, which provides guidelines for the efficient allocation of flexible resources and expand planning of CIES.

Thermal Comfort in a Tropical Climate: Case Study of the Cafeteria in Student Residential College, UTHM Pagoh Branch Campus

Thermal comfort is important for buildings, especially in tropical climates. The issue of increasing indoor temperature is influenced by various factors that cause occupants to face thermal discomfort in cafeterias that uses both mechanical and natural ventilation systems. This study aims to determine the perception of occupants and evaluate parameter variations of thermal comfort in the cafeteria of Pagoh Campus Residential College, UTHM Pagoh Branch Campus. This study was conducted through the measurements of subjective and physical parameters. Data collection is carried out in two days, which are weekdays and weekends. This study uses some parameters variation which are thermal sensation vote index (TSV), predicted mean vote index (PMV), predicted percentage of dissatisfaction (PPD), operative temperature (To) and neutral operative temperature (Tneutop) to determine the thermal comfort conditions in the cafeteria. The results shows that the average values of air temperature, mean radiation temperature, relative humidity and air velocity from environmental measurements were 30.2 °C, 30.6 °C, 66% and 0.1 m/s respectively. In addition, PMV and PPD values were 2.0 and 76% from environmental measurements, while 1.36 and 43% from questionnaires. For To and Tneutop, the average values obtained are 30.4 °C and 28.6 °C. Furthermore, the findings showed that only the morning session was in the acceptable operative temperature range and complied with ASHRAE Standard 55 for both the 80% and 90% acceptability limits. At the end of the study, it was found that the values from the thermal environment measurements and surveys exceeded those recommended by ASHRAE Standard 55. This concluded that the quality of the occupants’ environment in the cafeteria is outside the satisfactory thermal comfort level. Therefore, the improvement through mechanical ventilation may very helpful in improving a good ventilation system as well as encouraging an increase in thermal comfort in the building.

Urban microclimate analysis: residential block morphology impact on outdoor thermal comfort

In many cities, protracted exposure of urban structures to excessive solar radiation and the urban heat island (UHI) phenomenon may cause thermal discomfort for pedestrians in outdoor spaces, particularly in hot-arid regions. This study investigated the impact of urban residential block morphology on microclimate in Isfahan, Iran, using ENVI-met numerical models. The predicted mean vote (PMV) model for residential outdoor spaces was calculated using simulation data for the hottest day of the summer to assess outdoor thermal comfort. Comparison of the alternatives – namely, diagonal blocks, compact linear blocks, continuous curved blocks and dispersed cubic blocks – showed that the microclimate performance of the diagonal type was better than those of the other design options, as the PMV index represented the minimum distance from the ideal thermal comfort range due to a lower sky view factor (SVF) and optimal orientation. The curved blocks performed better in urban open spaces, promoting airflow and reducing UHI, than linear and cubic blocks. The results showed that the PMV index could predict thermal comfort in various urban design patterns and could be affected by the morphology of urban blocks. Variations in building morphology alter the SVF and microclimate parameters, which have an impact on outdoor thermal comfort.

Economic Model Predictive Control in Buildings Based on Piecewise Linear Approximation of Predicted Mean Vote Index

Energy shortage is a challenge for many countries, and building energy consumption accounts for a considerable proportion of global energy consumption. The main work of this paper is to optimize the energy consumption of heating, ventilating, and air conditioning (HVAC) systems in buildings based on economic model predictive control (EMPC). The cost in EMPC design includes energy consumption and predicted mean vote (PMV), which is an index that evaluates the thermal comfort of indoor occupants. In order to model the nonlinearity of the PMV index, we propose a lattice piecewise linear (PWL) approximation, which has high approximation precision and facilitates the resulting optimization problem, which is basically a piecewise quadratic programming problem. For the piecewise quadratic programming, we propose a descent algorithm that converges quickly and scales well with the length of the prediction horizon in the EMPC problem. The experimental results demonstrate that the proposed method saves 19.78% of the electricity cost compared to the conventional control strategy and significantly increases indoor comfort. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> — The motivation of this article is to provide a control strategy to reduce building energy consumption and ensure indoor thermal comfort. In most of the existing methods for air conditioning temperature control, the occupants’ comfort hasn’t been considered. In this paper, thermal comfort is described by the PMV index, which is basically nonlinear. In order to model the thermal comfort more accurately, in this paper, the PMV index is approximated piecewise linearly in order to meet the requirements of accuracy and computational efficiency. The resulting optimization problem is not hard to solve, and we provide an efficient algorithm for solving this optimization problem. Preliminary simulation experiments demonstrate that this approach is practical, i.e., it achieves energy reduction and ensures thermal comfort. Our strategy, however, has not yet been deployed in real buildings. In future research, we will propose similar techniques for large-scale systems in order to solve energy optimization problems containing multiple thermal zones and realize the proposed technique in real buildings.

Research on indoor thermal comfort of expressway service building based on SPMV

According to the characteristics of “temporary stay” and “streamlined” passenger flow in expressway service buildings, this article uses the “streamlined thermal comfort” index to characterize the indoor thermal comfort condition of the buildings,to create an overall comfortable space based on the “streamline trajectory”.The article proposes to divide comprehensive thermal comfort into “regional static thermal comfort” and “inter-zone dynamic thermal comfort”, revise the existing PMV(Predicted Mean Vote) index, and integrate the characteristics of the two types of indicators to form a comprehensive thermal comfort evaluation index based on “passenger trajectory”. The main work of this article includes: Firstly, using the temperature frequency method, a regression curve fitting between actual thermal sensation and indoor operative temperature is conducted to obtain the acceptable thresholds of MTS(Median Thermal Sensation), PMV neutral temperature, and actual temperature, forming a streamlined static thermal comfort index. The metabolic rate M value is segmented according to a 6-minute stabilization time to form a streamlined dynamic thermal comfort index; Introduce the factor of regional residence time τ to form a comprehensive thermal comfort index for streamlining. Secondly, an empirical analysis was conducted on the revised indicators, and the measured TSV(Thermal Sensation Vote) = -1, 0, 1 curves under different operating conditions in summer and winter were fitted with the SPMV(Streamline-Predicted Mean Vote) curve, revealing that the error in each functional area was within 10 %. The analysis results show that the modified index SPMV has greatly improved its consistency with the measured thermal sensation TSV, and can be used to evaluate the indoor thermal comfort of expressway service buildings in cold regions.The research results have high practical value for creating comfortable indoor environments in different indoor spaces of transportation commercial complexes, and have universal significance for creating streamlined indoor dynamic comfortable environments in urban commercial complexes.

Validity of ten analytical heat stress indices in predicting the physiological parameters of people under various occupational and meteorological conditions.

Until now, only a few comprehensive studies have validated analytical heat stress indices in different conditions. The present study aims to investigate the validity of these indicators in predicting the physiological parameters of workers. This cross-sectional study was conducted with 194 male employees working in warm environments. First, demographic information was collected. After participants rested for 30 min, their heart rate and tympanic temperature were measured. The subjects then performed their routine tasks. At the end of 90 min, their heart rate and tympanic temperature were again measured. Additionally, their metabolism rate and clothing thermal insulation were estimated. Environmental parameters were also measured at 30-, 60-, and 90-min time points. Additional information required to compute the indices was recorded. Then, the values of each of the indices were computed. Finally, the validity of the indices was assessed under different conditions. The results indicated that the highest regression coefficients with tympanic temperature were assigned to modified physiologically equivalent temperature (mPET) (0.7515), predicted heat strain (PHS) (0.7201), and predicted mean vote (PMV) (0.7082), index, respectively. Also, the greatest regression coefficients with heart rate belonged to mPET (0.7773), PMV (0.7624), and PHS (0.6479) index, respectively. Based on the results, the highest diagnostic accuracies of receiver operating characteristic (ROC) curves for tympanic temperature were related to indices of mPET, PHS, and PMV with the area under the ROC curve (AUC) of 0.945, 0.931, and 0.930, respectively. Of the studied indices, it was observed that mPET, PHS, PMV, and PPD showed more validity compared to others.

Experimentally investigating and numerically simulating the performances of various low-temperature hydronic heating/cooling systems connected to a vertical closed-loop ground-coupled heat pump

The method used in this study consists of evaluating different terminal heating/cooling systems in an experimental office at the Polytechnic University of Timișoara, Romania, through experimentation and numerical simulation. This paper is focused on the energy efficiency exploratory research of various low- and very low-temperature hydronic radiant heating/cooling systems (floor, wall, ceiling, combined floor-ceiling) and of the medium-temperature radiator heating system, connected to a vertical ground-coupled heat pump (GCHP) in double U-tube borehole heat exchanger (BHE) configuration. The main performance parameters are obtained for five weeks of operation in heating mode and three weeks in cooling mode, and a comparative analysis of these parameters together with indoor thermal comfort is carried out. The four simple heating systems (radiator, floor, wall, ceiling) have relatively small differences (maximum 7.4 %) in their coefficient of performance (COP) values, but the number of starts/stops in the case of radiator heating is twice that of floor heating, which leads to greater wear and tear on the heat pump equipment. Under the same operating conditions, the CO2 emission of the radiator heating system are 0.7–16 % higher than those of the heating system with radiant panels (floor, wall, and ceiling). The floor-ceiling combined radiant heating system has the highest COPsyst of 5.45 as well as the lowest CO2 emission value of 2.15 kg. The predicted mean vote (PMV) index (pair 1.1 met-0.29 clo) has values approximately equal to 0 only in the case of the floor-ceiling heating, when the percent of likely dissatisfied with the thermal comfort is 5 %. The three cooling radiant systems show differences in their energy performance, namely the radiant ceiling cooling system has the best COPsyst, 39.8 % higher than that of the radiant floor and only 5.9 % higher than that of the radiant wall. The values of the PMV index (pair 1 met-0.9 clo) for the radiant wall cooling system are 31–41 % lower than those of the radiant floor and 10.4–14.2 % lower than those of the radiant ceiling. Finally, numerical simulations of the useful thermal energy and the system COP in heating and cooling modes are performed with TRNSYS software for a duration of 8760 h and then analysed and compared with the experimental tests to validate the simulation models.

Personal differences in thermal comfort perception: Observations from a field study in Brisbane, Australia

Despite substantial evidence emphasising the diversity in thermal comfort perception, conventional models like the Predicted Mean Vote (PMV) index tend to overlook personal variability. In buildings, this oversight leads to occupant discomfort, dissatisfaction, and inefficient energy use for conditioning. In the past, technological and logistical constraints made personalised field studies an arduous task. However, today recent advancements in low-cost sensor technology allow one to pervasively capture the immediate and personal thermal environment experienced by building occupants. Between summer and winter 2022, we conducted a field study across 13 office buildings in Brisbane, Australia, investigating the personal variability in thermal comfort perception. Throughout the study, we recorded over 135,000 samples of continuous personal indoor environmental data at participants' workspaces and collected 813 occupant survey responses. Analysing the data, we found (1) significant personal variability between subjects, evident in a personal mean discrepancy from the PMV index ranging from −1.5 to +1.5 on the thermal comfort scale; (2) a limited accuracy of the PMV index ranging between 35 and 40%; when predicting the subject’s thermal sensation vote, (3) offices with highly heterogeneous thermal indoor environments; (4) dissatisfaction with the workspace due to thermal discomfort and insufficient control of the personal thermal environment. Our findings suggest a high personal and spatial variability in thermal comfort, underscoring the need to consider these factors to improve the accuracy and occupant-focus of thermal comfort indices.

Mitigation of Climate Change Impact on Bioclimatic Conditions Using Different Green Space Scenarios: The Case of a Hospital in Gorgan Subtropical Climates

Urban development and its climatic consequences have caused urban decision-makers to establish strategies to mitigate climate change. The implementation of different green spaces is one of the main strategies to reduce the environmental and climatic consequences of urbanization. Therefore, the main objective of this research is to reveal the effect of different green space scenarios on micro-bioclimatic conditions of a hospital located in Gorgan city, Golestan province. Therefore, in order to determine the position of the hospital building relative to Gorgan’s urban heat island (UHI), the location and changes in UHI intensity of Gorgan were determined as evidence of urban expansion. Since 27 July was determined as the hottest day in Gorgan city based on historical data analysis, the climatic conditions during 27 July 2021 were measured using an AR847 data logger installed in the hospital environment. Additionally, four different conditions, including actual environmental conditions of the hospital (actual conditions), along with the application of cypress trees (scenario A), plane trees (scenario B), and Buxus shrubs (scenario C), have been used to analyze the impact of different vegetation species on the bioclimatic conditions of 5 Azar Hospital during two time intervals, including observational periods (1970–2020) and the decade of the 2040s. Finally, spatiotemporal patterns of the predicted mean vote (PMV) thermal index were calculated for the observational period and during the 2040s using the ENVI-met micro-scale model. Results showed that the study site is in the UHI, which can affect the micro-bioclimatic conditions and the patient’s thermal perception. For all designed scenarios, results indicate that the average PMV index will increase by the 2040s. However, implementing different green space scenarios showed that the minimum and maximum values of PMV were found in scenario B, of 2.7. The actual PMV conditions of the studied site increased by 3.5. The scenario introduction of green spaces during the 2040s indicates that the average PMV at the hospital site will be decreased by 0.9 compared to the actual conditions. The study proves that appropriate green space strategies can reduce thermal loads occurring due to global climate change and improve the thermal conditions in the study area.

Performance in generation: An automatic generalizable generative-design-based performance optimization framework for sustainable building design

Performance-based design has become increasingly important for designing environmentally friendly buildings. It is an interdisciplinary and time-consuming process that requires architects to possess not only knowledge of design, performance simulation and optimization, but also the ability to make decisions that effectively align with environmental goals while exploring a range of design schemes. To promote such kind of design, this study proposes an innovative generalizable generative-design-based performance optimization framework (GGPOF). The framework combines generative design with performance-based design techniques to enhance various environmental aspects of buildings, including thermal comfort, daylighting and solar radiation. To facilitate performance-based design and optimization, the GGPOF automatically generates building variants and parametric simulation models, quick iteration with simulation feedback, sensitivity analysis and multi-objective optimization. A case study with two design scenarios demonstrates the high efficiency of GGPOF in green building design. More specifically, GGPOF can automatically generate every optimized design solution in an average time of 1.72 min. The optimal designs also achieve remarkable improvements in the predicted mean vote index for thermal comfort and the daylight factor compliance rate with increases of up to 86.57% and 72.73%, respectively, compared to the original user-defined design. The innovations of this study are as follows: first, it proposes a systematic and comprehensive solution for optimizing building performance from the architects' design perspective; second, it develops an automatic generalizable performance optimization framework at the early design stage.

Optimal Control Strategy for Ship Cabin’s Active Chilled Beam System Using Improved Multi-Objective Salp Swarm Algorithm

Heating, ventilation, and air conditioning (HVAC) systems are the second largest energy consumers on cruise ships after the propulsion system. As a kind of HVAC system, active chilled beam (ACB) systems have been widely used for cabin service due to their performance of energy efficiency and good thermal comfort. However, conventional control strategies for ships’ ACB systems are not intelligent enough and cannot balance energy consumption and cabin comfort during the voyages of ships. This study developed an optimal control strategy for cabins’ ACB systems. First, a simulation environment considering dynamic conditions is established in TRNSYS. Second, an artificial neural network model is utilized to predict the energy consumption of the ACB system, while the predicted percentage dissatisfied is adopted to represent cabins’ thermal discomfort. Third, an improved multi-objective salp swarm algorithm is proposed to dynamically minimize both energy consumption and thermal discomfort. A TRNSYS–MATLAB co-simulation testbed is established to simulate the cabins served by an ACB system on a small cruise ship navigating from Hong Kong to Shanghai for validation tests and a comparison study. Compared to the conventional strategies, the proposed strategy can achieve a maximum energy savings of 12% while maintaining a predicted mean vote index less than 0.5, meeting the comfort requirements set by ASHRAE.

Data-driven adaptive GM(1,1) time series prediction model for thermal comfort.

In this paper, the future prediction of predicted mean vote (PMV) index of indoor environment is studied. PMV is the evaluation index used in this paper to represent the thermal comfort of human body. According to the literature, the main environmental factors affecting PMV index are temperature, humidity, black globe temperature, wind speed, average radiation temperature, and clothing surface temperature, and there is a complex nonlinear relationship between the six variables. Due to the coupling relationship between the six parameters, the PMV formula can be simplified under specific conditions, reducing the monitoring of variables that are difficult to observe. Then, the improved grey system prediction model GM(1,1) with optimized selection dimension is used to predict the future time of PMV. Due to the irregularity, uncertainty and fluctuation of PMV values in time series, based on the original GM(1,1) time series prediction, an adaptive GM(1,1) improved model is proposed, which can continuously change with time series and enhance its prediction accuracy. By contrast, the improved GM(1,1) model can be derived from the sliding window of the adaptive model through changes in the dataset and get better model grades. It lays a foundation for the future research on the predicted index of PMV, so as to set and control the air conditioning system in advance, to meet the intelligence of modern intelligent home and humanized function of sensing human comfort.

Evaluation of thermal comfort in air-conditioned rooms based on structure/control-related parameters and data-mining method

Evaluating the thermal environment and thermal comfort in an air-conditioned room is an essential for estimating the performance of air-conditioning systems. However, multiple component structures and control-related parameters often lead to a long test cycle and large number of tests, significantly affecting the testing efficiency and speed. To address these problems, in this study, a data-mining method was proposed to predict and evaluate the thermal environment of an air-conditioned room. Owing to the limited amount of experimental data, the sample data were expanded by the simulation data of a collaborative platform between the air-conditioning system and air-conditioned room. Data-mining models, including the support vector regression (SVR), backpropagation (BP), and multiple linear regression (MLR) models, were developed and achieved good accuracy in evaluating the thermal environment by considering air-conditioning systems with various structures and control parameters. In the multiple-input single-output evaluation method, the prediction accuracy of the SVR model was higher than those of the BP and MLR models with respect to the vertical air temperature difference, temperature uniformity, temperature drop rate, and draft rate, while the result was the opposite in terms of the predicted mean vote indices. In the multiple-input multiple-output evaluation method, there was a decline in prediction accuracy and an increase in efficiency prediction compared with multiple-input single-output evaluation.

Assessment of green design strategies to achieve thermal comfort in outdoor spaces: A study in hot and dry climate

Outdoor spaces in cities contribute to improve urban quality. Improving outdoor thermal comfort is important to consider in achieving urban life and functionality. In order, green areas have shown to be effective at reducing the effects of several climatic elements associated with heat stress in urban settings while also giving people a great outdoor setting. The most basic problem is that most recent projects in Constantine cities did not take into account the usage of green design solutions, causing in human outdoor heat discomfort. This study aims to enable designers to ameliorate the conditions in outdoor spaces. The main objective of this paper is to discuss and assess the impact of geometry and shade trees on the external thermal comfort conditions due to the significant effects of thermal comfort on citizens, especially in hot and dry regions. The methodology is based on in-situ measurements and a questionnaire survey during the warm season. A simulation with Envi-met5 software to evaluate different scenarios for five climatic parameters and PMV (predicted mean vote index) are used. The results showed that the presence of trees is an important strategy to modify and regulate the thermal environment in this type of hot and dry climate.

Two-Stage Low-Carbon Economic Dispatch of Integrated Demand Response-Enabled Integrated Energy System with Ladder-Type Carbon Trading

Driven by the goal of “carbon neutrality” and “emission peak”, effectively controlling system carbon emissions has become significantly important to governments around the world. To this end, a novel two-stage low-carbon economic scheduling framework that considers the coordinated optimization of ladder-type carbon trading and integrated demand response (IDR) is proposed in this paper for the integrated energy system (IES), where the first stage determines the energy consumption plan of users by leveraging the price-based electrical-heat IDR. In contrast, the second stage minimizes the system total cost to optimize the outputs of generations with consideration of the uncertainty of renewables. In addition, to fully exploit the system’s emission reduction potential, a carbon trading cost model with segmented CO2 emission intervals is built by introducing a reward-penalty ladder-type carbon trading mechanism, and the flexible thermal comfort elasticity of customers is taken into account by putting forward a predicted mean vote index on the load side. The CPLEX optimizer resolves the two-stage model, and the study results on a modified IES situated in North China show the proposed model can effectively reduce carbon emissions and guarantee economical efficiency operation of the system.

Analysis of predicted mean vote-based model predictive control for residential HVAC systems

Model Predictive Control (MPC) is an advanced process control method that has attracted much attention in building heating, ventilation, and air conditioning (HVAC) systems. This paper analyzes the optimal precooling performance in residential buildings using MPC with two different comfort indices, namely, temperature and predicted mean vote (PMV). It first formulates, for each comfort index, an optimization problem that accounts for different factors, such as weather, home thermal condition, prediction horizon, time-of use (TOU) utility rate, and rated cooling capacity. The problem is then solved, resulting in an MPC strategy that determines the HVAC on/off control signal and minimizes energy cost over a receding time horizon while maintaining thermal comfort. The energy performance difference between temperature-based and PMV-based MPC strategies is subsequently investigated, especially in light of the interior wall surface temperature and under different combinations of the factors. Extensive simulation results demonstrated that the proposed MPC strategies are adaptive and their performances depend primarily on weather, home thermal condition, and prediction horizon, while the impact of TOU utility rate and rated cooling capacity is relatively small. Because the PMV-based MPC strategy can take advantage of the lower interior wall surface temperature due to precooling, it resulted in 8–45% cost savings for the scenarios investigated and an average increase of 0.042–0.113 in the absolute value of the PMV index compared to the temperature-based MPC strategy.