bluuebunny/crossref_metadata_2025_split huggingface dataset:
A review on coupled building physics analyses
Authors: Bühler M. , Bednar T. 
Published: December 2021

Abstract
This paper reviews methods and tools for coupled building physics analyses in the context of Building Performance Simulations (BPS) with a focus on Building Energy Simulations (BES) and Computational Fluid Dynamics (CFD) as a common application. Furthermore, requirements regarding the necessary information for simulations, data models and coupling are identified. Possibilities of automated simulation model generation, data exchange and the performance of existing multi physics simulation models are analysed and limiting factors are discussed.
Anomaly detection and missing data imputation in building energy data for automated data pre-processing
Authors: Takahashi K , Ooka R , Ikeda S 
Published: December 2021

Abstract
A new trend in building automation is the implementation of smart energy management systems to measure and control building systems without a need for decision-making by human operators. Artificial intelligence can optimize these systems by predicting future demand to make informed decisions about how to efficiently operate individual equipment. These machine learning algorithms use historical data to learn demand trends and require high quality datasets in order to make accurate predictions. But because of issues with data transmission or sensor errors, real world datasets often contain outliers or have data missing. In most research settings, these values can be simply omitted, but in practice, anomalies compromise the automation system’s prediction accuracy, rendering it unable to maximize energy savings. This study explores different machine learning algorithms for anomaly detection for automatically pre-processing incoming data using a case study on an actual electrical demand in a hospital building in Japan, namely cluster-based techniques such as k-means clustering and neural network-based approaches such as the autoencoder. Once anomalies were identified, the missing data was filled with prediction values from a deep neural network model. The newly composed data was then evaluated based on detection accuracy, prediction accuracy and training time. The proposed method of processing anomaly values allows the prediction model to process collected data without interruption, and shows similar predictive accuracy as manually processing the data. These predictions allow energy systems to optimize HVAC equipment control, increasing energy savings and reducing peak building loads.
Study of power demand forecasting of a hospital by ensemble machine learning
Authors: Nakai Mayuka , Ooka Ryozo , Ikeda Shintaro 
Published: December 2021

Abstract
To save energy in existing buildings, power demand can be predicted so a more efficient operation of equipment can be realized, like utilizing heat storage to lower the peak. Many attempts to predict building power consumption by machine learning have used simulation values in virtual buildings with no measurement errors or defects in the data. These models tend to have higher accuracy scores but have the risk of overfitting and possibly malfunction for missing data or outliers. To avoid the problems, this study proposes an ensemble machine learning algorithm to forecast power demand for a hospital building in Japan. Using the power consumption data, predictions were made by using algorithms such as Deep Neural Network (DNN) and Random Forest (RF). Each algorithm was combined to create ensemble models that take the weighted average of the predicted values. Consequently, we overcame the issues of each individual method, and achieved higher prediction accuracies. We selected the appropriate method for forecasting the power demand of real buildings based on accuracy. In future studies, we will apply the same methodology to predict cooling load.
Empowering energy flexibility and climate resilience using collective intelligence based demand side management (CI-DSM)
Authors: Nik Vahid M. , Moazami Amin 
Published: December 2021

Abstract
This work investigates the effectiveness of Collective intelligence (CI) in demand side management (DSM) in urban areas to cope with extreme climate events. CI is a form of distributed intelligence that emerges in collaborative problem solving and decision making. It is used in a simulation platform to control the energy performance of buildings in an urban area in Stockholm, through developing CI-DSM and setting certain adaptation measures, including phase shifting in HVAC systems and building appliances. CI-DSM is developed based on a simple communication strategy among buildings, using forward (1) and backward (0) signals, corresponding to applying and disapplying the adaptation measures. The performance of CI-DSM is simulated for three climate scenarios representing typical, extreme cold and extreme warm years in Stockholm. According to the results, CI-DSM increases the autonomy and agility of the system in responding to climate shocks without the need for computationally extensive central decision making systems. CI-DSM helps to gradually and effectively decrease the energy demand and absorb the shock during extreme climate events.
Low-cost smart solutions for daylight and electric lighting integration in historical buildings
Authors: Scorpio Michelangelo , Ciampi Giovanni , Gentile Niko , Sibilio Sergio 
Published: December 2021

Abstract
Research have shown that the correct integration of daylight and electric lighting reduces the energy use in buildings, while improving visual comfort. Smart shading systems, especially those electrically controlled, play an important role to control solar radiation. Similarly, smart and dimmable/tunable lighting can help to adjust the artificial light to the real users’ needs. This paper presents preliminary results of an ongoing living lab study investigating how artificial lighting systems can be integrated with shading systems, placing human comfort at the heart of the study and yet saving energy. A manually controlled, commercial and low-cost smart system integrating two motorized shading devices and six dimmable LED luminaires with a different selection of CCT were installed in a private office in a historical building. Indoor and outdoor lighting conditions and energy consumption associated to the lighting system are constantly monitored to assess how the people use shading and lighting upon varying the boundary conditions.. Preliminary results highlight that users prefer to maximise daylight on the work plane as well as they generally use both shading and electric lighting systems in response to boundary conditions that cause serious discomfort.
Effect of competitive acoustic environments on speech intelligibility
Authors: Puglisi G E , Warzybok A , Astolfi A , Kollmeier B 
Published: December 2021

Abstract
Excessive noise and reverberation times degrade listening abilities in everyday life environments. This is particularly true for school settings. Most classrooms in Italy are settled in historical buildings that generate competitive acoustic environments. So far, few studies investigated the effect of real acoustics on speech intelligibility and on the spatial release from masking, focusing more on laboratory conditions. Also, the effect of noise on speech intelligibility was widely investigated considering its energetic rather than its informational content. Therefore, a study involving normal hearing adults was performed presenting listening tests via headphone and considering the competitive real acoustics of two primary-school classrooms with reverberation time of 0.4 s and 3.1 s, respectively. The main objective was the investigation of the effect of reverberation and noise on the spatial release from masking to help the design of learning environments. Binaural room impulse responses were acquired, with noise sources at different azimuths from the listener’s head. The spatial release from masking was significantly affected by noise type and reverberation. Longer reverberation times brought to worst speech intelligibility, with speech recognition thresholds higher by 6 dB on average. Noise with an informational content was detrimental by 7 dB with respect to an energetic noise.
Binaural Speech Intelligibility in a Real Elementary Classroom
Authors: Minelli G , Puglisi G E , Astolfi A , Hauth C , Warzybok A 
Published: December 2021

Abstract
Since the fundamental phases of the learning process take place in elementary classrooms, it is necessary to guarantee a proper acoustic environment for the listening activity to children immersed in them. In this framework, speech intelligibility is especially important. In order to better understand and objectively quantify the effect of background noise and reverberation on speech intelligibility various models have been developed. Here, a binaural speech intelligibility model (BSIM) is investigated for speech intelligibility predictions in a real classroom considering the effect of talker-to-listener distance and binaural unmasking due to the spatial separation of noise and speech source. BSIM predictions are compared to the well-established room acoustic measures as reverberation time (T30), clarity or definition. Objective acoustical measurements were carried out in one Italian primary school classroom before (T30= 1.43s±0.03 s) and after (T30= 0.45±0.02 s) the acoustical treatment. Speech reception thresholds (SRTs) corresponding to signal-to-noise ratio yielding 80% of speech intelligibility will be obtained through the BSIM simulations using the measured binaural room impulse responses (BRIRs). A focus on the effect of different speech and noise source spatial positions on the SRT values will aim to show the importance of a model able to deal with the binaural aspects of the auditory system. In particular, it will be observed how the position of the noise source influences speech intelligibility when the target speech source lies always in the same position.
Experimental investigation of occupants’ thermal sensation under a personalized ventilation system
Authors: Ebrahimi Naghani P , Zolfaghari S A , Maerefat M , Toftum J , Hooshmand S M , Izadi M 
Published: December 2021

Abstract
By considering the importance of providing proper indoor environment conditions for occupants and also due to energy costs, one of the solutions for indoor local air-conditioning is Personalized Ventilation System (PVS). In this paper, the occupants’ thermal sensation was experimentally studied for body segments that are mostly affected by the PVS. The local sensation of head, chest, arm, and hand at two supply air temperatures of 16 and 32°C were investigated. Eight volunteer subjects participated in this survey. The subjects reported the most thermal satisfaction on their hands. Also, the arms were the segments with the coolest thermal sensation (-1.28, between slightly cool and cold). Results indicate that the head’s thermal sensation at both supply air temperatures was neutral and the hand was the only body part that experienced warm thermal sensation during the test. Also, by increasing the supply air temperature to 32°C whole body thermal sensation changed from -0.46 to -0.09 on the seven-point scale, which means that the cooling system worked properly for occupants’ cooling. In this system, cooling occurred at 32°C instead of the common 16°C supply air temperature, which results in energy-saving and decreases annual running costs.
The role of complex airflow simulation tools for overheatingassessment of passive houses
Authors: Goncalves V , Rakha T 
Published: December 2021

Abstract
Passive Houses are characterized mainly by construction concepts that greatly reduce energy usage during the winter, but that can lead to significant overheating during the hotter summer days. Since in the Passive House concept thermal comfort during the summer mainly relies on natural ventilation to provide indoor cooling, the importance of airflow modeling tools for overheating prediction needs to be investigated. This research analyzes the effect of simplifications commonly made in airflow modeling techniques on the overheating assessment of Passive Houses by collecting measured data and calibrating a thermal model with a Passive House case study. Utilizing the calibrated model, a standalone Building Energy Model (BEM), BEM coupled with an Airflow Network Model (AFN), and BEM coupled with an AFN supported by the wind pressure coefficient values obtained from Computational Fluid Dynamics (CFD) simulation were created. The outcome of each modeling approach was then compared against each other. Results showed that the default infiltration and natural ventilation input values commonly utilized in literature, when compared to those obtained from either the AFN or AFN+CFD, are significantly overestimating the natural ventilation potential of Passive House buildings, resulting in a lower number of overheating hours (39.9% decrease) and inaccurate overheating evaluation outcomes. Therefore, the paper concludes that the use of at least an AFN is necessary when estimating the overheating hours of Passive Houses.
Geographical distribution of the sources of IEQ complaints: an analysis through text-mining of online job reviews
Authors: Chinazzo Giorgia 
Published: December 2021

Abstract
Text-mining allows analyzing a large amount of non-structured data, such as online reviews, to gain insights about previously unknown information. Online job reviews contain a variety of information, ranging from salary estimations to interview experiences. Among this information, the text posted online can report an evaluation of the workplace’s indoor environmental quality (IEQ), describing both its positive and negative aspects. When referring to negative characteristics, online reviews can be considered to report IEQ complaints. Such complaints can be categorized according to the four IEQ aspects (i.e., thermal, visual, acoustic, and indoor air quality) and their combination. This paper exploits text-mining techniques to investigate the geographical distribution of the sources of IEQ complaints according to the location in which the job review is posted. The analysis is performed in terms of climate (according to the Köppen-Geiger climate classification), country, and population (to consider the distribution between high-density and low-density areas). The results show that the distribution of the source of IEQ complaints varies according to the climate and the country, even though thermal aspects are always the largest source of discomfort in all countries and climatic zones. The more significant rates of thermal complaints are observed in the U.S. and India. They could be associated with the extensive use of HVAC systems and the restrictive operating temperatures adopted in these countries. The results also show that acoustic, indoor air quality and visual complaints are more numerous in large cities than in rural areas, where thermal complaints prevail. This paper provides a picture of the current IEQ discomfort across several geographical regions and highlights the great potential of User-Generated-Content to study various aspects of the IEQ, in this case, their geographic distribution.
Development and test of a combined radon sub-slab suction and sub-slab drainage system
Authors: Høegh B H , Hansen T , Clausen S 
Published: December 2021

Abstract
To ensure a healthy indoor environment, the indoor air level of the radioactive gas radon must be kept low according to the WHO. This can be achieved by installing a radon sub-slab suction system. In buildings with a basement at the same time a sub-slab drainage system is often necessary. This paper describes results from a project, aiming to combine a radon sub-slab suction system with a sub-slab drainage system. A combined system will minimize the number of pipes when constructing new buildings and will also provide an easier retrofitting method for adding a radon sub-slab suction system to buildings with an existing sub-slab drainage system. In the project, it was found that the combination of the two functionalities required an airtight system to lower the pressure under the ground slab, an unhampered drainage of ground water and a prevention of odour from the drains. To meet these requirements, a prototype of a well with a water trap, a water outlet and a separate suction pipe for the air outlet was developed. A low voltage fan was installed in the suction pipe. The system was installed in a detached house with a 104 m2 basement. After installation, the pressure reduction over the ground slab in the basement was measured to be able to investigate the effect of the suction system independently of the radon exposure. The results showed a reduction of the pressure in the farthest corners under the ground slab by approximately 0.6 to 1.9 Pa compared to the pressure over the ground slab. We concluded that a combined radon sub-slab suction and sub-slab drainage system is possible with the designed well, although the use of a stronger fan will be necessary to meet the identified test objective of pressure reduction ΔP ≥ 1-3 Pa.
On the Benefits of Whole-building IAQ, Ventilation, Infiltration, and Energy Analysis Using Co-simulation between CONTAM and EnergyPlus
Authors: Stuart Dols W. , Milando Chad W. , Ng Lisa , Emmerich Steven J. , Teo Jyrteanna 
Published: December 2021

Abstract
Publicly available tools to perform whole-building simulation of indoor air quality, ventilation, and energy have been available for several decades. Until recently, these tools were developed in isolation, such as the whole-building contaminant transport and airflow analysis tool, CONTAM, developed by the National Institute of Standards and Technology (NIST) and the whole-building energy analysis tool, EnergyPlus, developed by the U.S. Department of Energy (DOE). The ability to couple these tools during runtime has been implemented through co-simulation, enabling improved analysis of the interdependent effects of temperature and airflow on contaminant transport and energy use on a whole-building scale.
This presentation will include the development of a set of coupled reference building models for the purposes of evaluating the potential benefits of using co-simulation between CONTAM and EnergyPlus. A set of Residential Prototype Building Models available from DOE has been modified by NIST and utilized to demonstrate the coupling process and the benefits of this coupling with respect to IAQ and energy analysis, and to evaluate multiple whole-building simulation methods related to infiltration, ventilation, and occupant exposure. These methods include an original EnergyPlus prototype model, the original model with NIST-based infiltration correlations, co-simulation between EnergyPlus and CONTAM, and stand-alone CONTAM simulations. Potential benefits will be explored related to the ability of co-simulation to address the effects of variations in building typology and ventilation system performance on contaminant transport results while leveraging the capabilities of whole-building energy analysis.
Application of developed facade panel from recycled CDW: A case study
Authors: Bagarić M , Banjad Pečur I , Milovanović B 
Published: December 2021

Abstract
Using waste materials for production of sustainable exterior façade panel, that can be recycled at the end of its life cycle as part of a circular economy model, can significantly reduce environmental footprint of buildings and help preserve natural resources. The envelope system under consideration is a ventilated prefabricated wall panel from recycled construction and demolition waste (CDW). In this paper, hygrothermal simulations together with field monitoring of hygrothermal performance, energy consumption, indoor comfort and air quality in real environment conditions have been presented. Results show that developed panel is a robust, moisture-safe panel suitable for constructing energy high performing buildings. Thermal discomfort in summer is related to the architectural design of the building.
The effect of air conditioning outlets on the spread of respiratory disease in Mosque’s environment
Authors: Khan H M , Al-Saadi S N 
Published: December 2021

Abstract
Mosques are places for daily worship for Muslims, where they attend prayers five times/day. As a common prayer practice, worshippers conduct prayers in standing groups side-by-side in rows touching shoulders and ankles. Furthermore, in their praying practice, worshippers touch the floor with their forehead four to eight times in a single prayer, which is an important factor in picking or spreading infection disease. Mosques are usually air-cooled by mechanical means with a poor ventilation system. The prayer practices, coupled with the poor ventilation system increase the risk of spreading respiratory diseases like COVID-19. This study utilizes a Computational Fluid Dynamics (CFD) package to evaluate disease particles’ movement around rows of worshipers. The research evaluates the impact of air outlet locations on the spread of the disease particles. The results indicated that the locations of air outlets relative to the infected person may significantly help to spread the particulates. In mosque environment, the ceiling diffusers are recommended, and sidewall outlets should be avoided. In addition, it was concluded that a minimum of 2 meters between occupants as suggested by WHO is not deemed enough to control the spread of disease in mosque environment and a minimum of 3 m is necessary. The study calls to review the guidelines by the World Health Organization (WHO) for mosques and similar environment.
Numerical Model for Prediction of Indoor COVID-19 Infection Risk Based on Sensor Data
Authors: Virbulis J , Sjomkane M , Surovovs M , Jakovics A 
Published: December 2021

Abstract
In addition to infection with SARS-CoV-2 via direct droplet transmission or contact with contaminated surfaces, infection via aerosol transport is a predominant pathway in indoor environments. The developed numerical model evaluates the risk of a COVID-19 infection in a particular room based on measurements of temperature, humidity, CO2 and particle concentration, the number of people and instances of speech, coughs and sneezing using a dedicated low-cost sensor system. The model can dynamically provide the predicted risk of infection to the building management system or people in the room. The effect of temperature, humidity and ventilation intensity on the infection risk is shown. Coughing and especially sneezing greatly increase the probability of infection in the room; therefore distinguishing these events is crucial for the applied measurement system.
A simplified model to estimate COVID19 transport in enclosed spaces
Authors: Mirzaei P A , Moshfeghi M , Motamedi H , Sheikhnejad Y , Bordbar H 
Published: December 2021

Abstract
Airborne pathogen respiratory droplets are the primary route of COVID19 transmission, which are released from infected people. The strength and amplitude of a release mechanism strongly depend on the source mode, including respiration, speech, sneeze, and cough. This study aims to develop a simplified model for evaluation of spreading range (length) in sneeze and cough modes using the results of Eulerian-Lagrangian CFD model. The Eulerian computational framework is first validated with experimental data, and then a high-fidelity Lagrangian CFD model is employed to monitor various scale particles’ trajectory, evaporation, and lingering persistency. A series of Eulerian-Lagrangian CFD simulations is conducted to generate a database of bioaerosol release spectrum for the release modes in various thermal conditions of an enclosed space. Eventually, a correlation fitted over the data to offer a simplified airborne pathogen spread model. The simplified model can be applied as a source model for design and decision-making about ventilation systems, occupancy thresholds, and disease transmission risks in enclosed spaces.