Energy consumption and thermal comfort of rock-cut and modern buildings.

Energy consumption in the building sector justifies the necessity of knowing the thermal comfort perception of vernacular and modern architectural types, based on which a correct recognition was reached for the design of buildings suitable for the climatic conditions of each region. It should be determined that the different types of modern and traditional architecture are in the comfort level in harsh hot climate conditions and how much energy they consume to reach the comfort level. Despite consideration of energy consumption and thermal comfort in different buildings in Iran, there is no clear framework for evaluating these two parameters in different buildings and comparing them. This research aims to compare the indoor thermal comfort levels of vernacular architectural buildings and modern buildings in Iran's semi-hot and dry climate at the peak of summer heat and determine their energy consumption to reach the comfort level. This study has been accomplished by collecting field data, examining the indoor predicted mean vote (PMV) index of the buildings, and comparing them. It was found that rock-cut architecture buildings are in better thermal comfort conditions without energy consumption due to the use of groundmass temperature and low heat exchanges between the indoors and outdoors because of the thermal phase of the materials and the thickness of its layers. The indoor PMV average of rock-cut buildings in summer is -0.61; in modern buildings, it is 0.77, while these two building complexes are in the same climate and close. Also, the energy consumption to reach the comfort level in rock-cut buildings is zero, while modern buildings consume an average of 7.7 kW of electricity daily. The research results will lead to recognizing and modeling the climate design of vernacular architecture, which can be used in today's architecture to reduce energy consumption.

Indoor thermal responses and their influential factors-- impacts of local climate and contextual environment: A literature review.

Indoor thermal comfort/perception (ITC) would be affected by contextual factors. The present article reviewed findings (thermal responses represented by neutral temperature, NT) of ITC studies published in recent decades. There were two types of contextual factors: climate (latitude, altitude, and distance from the sea) and building characteristics (building type, and ventilation mode). Through associating NTs with their contextual factors, it was found that people's thermal responses were significantly affected by climatic factors, especially latitude in summers. Increasing latitude by 10° resulted in NT decrease of around 1 °C. Others- e.g., building characteristics- were influential to some degrees. Effects of ventilation mode (natural ventilated, NV; air-conditioned, AC) varied for the seasons. Generally, people in NV buildings had higher summer NTs, such as 26.1 °C in NV and 25.3 °C in AC in Changsha. The results demonstrated significant human adaptations to climatic and microenvironmental influences. The design and construction of future residences could be more fine-tuned with the building insolation and heating/cooling technology to fit the thermal preferences of local residents for the best internal temperature settings. This study's findings may potentially serve as a foundation for future ITC research.

Numerical Assessment of Zebra-Stripes-Based Strategies in Buildings Energy Performance: A Case Study under Tropical Climate.

Urban growth has increased the risk of over-heating both in the microclimate and inside buildings, affecting thermal comfort and energy efficiency. That is why this research aims to evaluate the energy performance of buildings in terms of thermal comfort (operative temperature (OP) levels, satisfied hours of natural ventilation SHNV, thermal lag), and energy efficiency (roof heat gains and surface temperatures) in an urban area in Panama City, using superficial-heat-dissipation biomimetic strategies. Two case studies, a base case and a proposed case, were evaluated using the Designbuilder software through dynamic simulation. The proposed case is based on a combined biomimetic strategy; the reflective characteristics of the Saharan ant applied as a coating on the roofs through a segmented pattern such as the Zebra's stripes (one section with coating, and another without). Results showed that the OP decreased from 8 to 10 °C for the entire urban zone throughout the year. A reduction of 3.13% corresponding to 8790 kWh per year was achieved for cooling energy consumption. A difference of 5 °C in external surface temperature was obtained, having a lower temperature in which the biomimetic strategy was applied. Besides, it was evidenced that a contrasted-reflectivity-stripes pitched roof performed better than a fully reflective roof. Thus, the functionality of Zebra stripes, together with the reflective characteristics of the Saharan ant, provide better performance for buildings' thermal regulation and energy needs for cooling.

Field Study on Indoor Thermal Environments of Monastic Houses and Thermal Comfort of Monks.

Monastic houses are an essential part of the Tibetan monastic system in China. In this study, the monastic houses of Labrang in the Tibetan region of Gannan were used as the research objects. Physical parameters such as indoor temperature, humidity, and radiation temperature of the monastic houses were measured. The measured results were compared with the standard values, while the air temperature was linearly fitted using TSV, PMV, and aPMV. The results show that the temperature inside and outside the monastic houses fluctuates considerably; the theoretical thermal neutral temperature of the tested monks in winter is 22.46 °C, which is higher than the measured thermal neutral temperature in winter of 16.43 °C. When analyzing the results, it was found that the local climate, dress code, and the monks' specific habits all impact the perception of thermal comfort, which creates a discrepancy between the accurate results and the standard values. The above findings provide a more comprehensive reference for the thermal comfort requirements of the monks in cold areas, which can be used as a guide for the improvement and evaluation of the monastic houses in cold areas.

Evaluation of the Visual Stimuli on Personal Thermal Comfort Perception in Real and Virtual Environments Using Machine Learning Approaches.

Personal Thermal Comfort models consider personal user feedback as a target value. The growing development of integrated "smart" devices following the concept of the Internet of Things and data-processing algorithms based on Machine Learning techniques allows developing promising frameworks to reach the best level of indoor thermal comfort closest to the real needs of users. The article investigates the potential of a new approach aiming at evaluating the effect of visual stimuli on personal thermal comfort perception through a comparison of 25 participants' feedback exposed to a real scenario in a test cell and the same environment reproduced in Virtual Reality. The users' biometric data and feedback about their thermal perception along with environmental parameters are collected in a dataset and managed with different Machine Learning techniques. The most suitable algorithm, among those selected, and the influential variables to predict the Personal Thermal Comfort Perception are identified. The Extra Trees classifier emerged as the most useful algorithm in this specific case. In real and virtual scenarios, the most important variables that allow predicting the target value are identified with an average accuracy higher than 0.99.

Uncertainty in the evaluation of the Predicted Mean Vote index using Monte Carlo analysis.

Today, evaluation of thermohygrometric indoor conditions is one of the most useful tools for building design and re-design and can be used to determine energy consumption in conditioned buildings. Since the beginning of the Predicted Mean Vote index (PMV), researchers have thoroughly investigated its issues in order to reach more accurate results; however, several shortcomings have yet to be solved. Among them is the uncertainty of environmental and subjective parameters linked to the standard PMV approach of ISO 7730 that classifies the thermal environment. To this end, this paper discusses the known thermal comfort models and the measurement approaches, paying particular attention to measurement uncertainties and their influence on PMV determination. Monte Carlo analysis has been applied on a data series in a "black-box" environment, and each involved parameter has been analysed in the PMV range from -0.9 to 0.9 under different Relative Humidity conditions. Furthermore, a sensitivity analysis has been performed in order to define the role of each variable. The results showed that an uncertainty propagation method could improve PMV model application, especially where it should be very accurate (-0.2 < PMV<0.2 range; winter season with Relative Humidity of 30%).

Integrated Method for Personal Thermal Comfort Assessment and Optimization through Users' Feedback, IoT and Machine Learning: A Case Study †.

Thermal comfort has become a topic issue in building performance assessment as well as energy efficiency. Three methods are mainly recognized for its assessment. Two of them based on standardized methodologies, face the problem by considering the indoor environment in steady-state conditions (PMV and PPD) and users as active subjects whose thermal perception is influenced by outdoor climatic conditions (adaptive approach). The latter method is the starting point to investigate thermal comfort from an overall perspective by considering endogenous variables besides the traditional physical and environmental ones. Following this perspective, the paper describes the results of an in-field investigation of thermal conditions through the use of nearable and wearable solutions, parametric models and machine learning techniques. The aim of the research is the exploration of the reliability of IoT-based solutions combined with advanced algorithms, in order to create a replicable framework for the assessment and improvement of user thermal satisfaction. For this purpose, an experimental test in real offices was carried out involving eight workers. Parametric models are applied for the assessment of thermal comfort; IoT solutions are used to monitor the environmental variables and the users' parameters; the machine learning CART method allows to predict the users' profile and the thermal comfort perception respect to the indoor environment.

Design and Development of a Nearable Wireless System to Control Indoor Air Quality and Indoor Lighting Quality.

The article describes the results of the project "open source smart lamp" aimed at designing and developing a smart object able to manage and control the indoor environmental quality (IEQ) of the built environment. A first version of this smart object, built following a do-it-yourself (DIY) approach using a microcontroller, an integrated temperature and relative humidity sensor, and techniques of additive manufacturing, allows the adjustment of the indoor thermal comfort quality (ICQ), by interacting directly with the air conditioner. As is well known, the IEQ is a holistic concept including indoor air quality (IAQ), indoor lighting quality (ILQ) and acoustic comfort, besides thermal comfort. The upgrade of the smart lamp bridges the gap of the first version of the device providing the possibility of interaction with the air exchange unit and lighting system in order to get an overview of the potential of a nearable device in the management of the IEQ. The upgraded version was tested in a real office equipped with mechanical ventilation and an air conditioning system. This office was occupied by four workers. The experiment is compared with a baseline scenario and the results show how the application of the nearable device effectively optimizes both IAQ and ILQ.

A Low-Cost Environmental Monitoring System: How to Prevent Systematic Errors in the Design Phase through the Combined Use of Additive Manufacturing and Thermographic Techniques.

nEMoS (nano Environmental Monitoring System) is a 3D-printed device built following the Do-It-Yourself (DIY) approach. It can be connected to the web and it can be used to assess indoor environmental quality (IEQ). It is built using some low-cost sensors connected to an Arduino microcontroller board. The device is assembled in a small-sized case and both thermohygrometric sensors used to measure the air temperature and relative humidity, and the globe thermometer used to measure the radiant temperature, can be subject to thermal effects due to overheating of some nearby components. A thermographic analysis was made to rule out this possibility. The paper shows how the pervasive technique of additive manufacturing can be combined with the more traditional thermographic techniques to redesign the case and to verify the accuracy of the optimized system in order to prevent instrumental systematic errors in terms of the difference between experimental and actual values of the above-mentioned environmental parameters.