Search

• Experiment in three homes using mixed-methods and physical measurements. • Indoor air temperature fluctuations like those of EMPC of space heating. • Night setback-users found the heating control unacceptable. • Acceptance was motivated by economic or environmental benefits. Recent simulation-based studies have indicated that Economic Model Predictive Control (EMPC) of space heating systems can exploit the thermal mass in residential buildings for demand response (DR) purposes in district heating systems. However, there is a lack of studies on residents' perception and acceptance of the fluctuating indoor air temperature behaviour inherent in EMPC of space heating. This paper reports on a case study featuring the residents of three one-story houses located in Denmark. The houses were equipped with technology enabling remote actuation of radiator thermostats and collection of various indoor environmental data. Four different temperature boost interventions mimicking the typical behaviour of EMPC of radiators were executed while a mixed-methods triangulation design, employing questionnaires and semi-structured interviews, was used to collect subjective data. Data showed that residents accepted the behaviour but only after the benefits of the EMPC were explained. The acceptance was motivated by either the prospect of saving money, environmental benefits, or a combination of both. The results thereby indicate that explaining the benefits of EMPC is important if residents are to abandon their current preferences and practices in favour of an EMPC concept. The explanation should consider the diversity in current preferences, expectations, and level of technological pre-knowledge. In addition, the paper also reports on a range of findings related to technical aspects of realising EMPC in practice. [ABSTRACT FROM AUTHOR]

Several studies have indicated that Economic Model Predictive Control (EMPC) of residential space heating systems can exploit the building thermal mass to provide demand response for the energy supply system by boosting the indoor air temperature prior to peak load periods. The practical uptake of EMPC is thus dependent on whether residents accept the fluctuating indoor air temperatures induced by this form of EMPC. Evidence on this aspect is scarce but a few studies indicate in hindsight that it might be important to explain the economic and environmental benefits of an EMPC if residents are to abandon their current preferences and practices in favour of an EMPC concept. The objective of the study reported in this paper was therefore to investigate whether residents of three single-family homes actually would accept the fluctuating indoor air temperature induced by EMPC when being aware of environmental and/or economic benefits. The results suggest that residents are willing to accept the fluctuating indoor air temperature being aware of economic and environmental benefits – as long as the fluctuations do not make them feel too cold. [ABSTRACT FROM AUTHOR]

• The average monthly daytime gap temperatures were always lower than ambient air (up to 4 °C). • During warm months, evapotranspirative cooling contributed 55% of the total gap cooling. • During winter days, external wall warming was observed behind the north-facing deciduous green façade. • simple linear correlations were found between ambient air and gap temperatures (R2 > 0.80). • The linear correlation between ambient air and gap temperatures was also predicted previously published gap temperatures very well (R2 0.98) The growing interest in energy savings and environmental sustainability in buildings has led to green buildings and green façades being considered as innovative solutions. This paper aimed to quantify the impact of ambient air temperature, orientation (west and north) and climbing plant status and type (leaf area index, percent coverage, deciduous and non-deciduous) on the thermal performance of green façades including external wall and gap cooling/warming effects. Temperatures of biological façades (i.e. green façade) and non-biological façades (i.e. shade sail) were monitored over 12 months in Perth, Western Australia, under a Mediterranean climate. Air temperatures within the gaps between the façade and external walls were also monitored. The average monthly daytime gap temperatures behind the green façades were always lower than ambient air and those behind the shade sails (up to 4 °C). The orientation of the façade relative to solar angle controlled the duration of solar irradiance; seasonal variations were observed in the relative contribution of evapotranspirative cooling to overall gap cooling, even in non-deciduous green façades. Despite the complex relationships between evapotranspirative cooling and a range of ecophysiological and micro-climate parameters, simple linear correlations were found between ambient air and gap temperatures (R2 > 0.8). This emphasizes the dominance of ambient air temperature in controlling the thermal performance of green facades. This thermal performance assessment and the gap temperature simulation can help urban designers, architects, and environmental engineers improve building and façade design to reduce energy consumption and greenhouse emissions. [ABSTRACT FROM AUTHOR]

• Statistical analysis of the performance of a green wall in a Mediterranean climate. • Irradiance is discarded as a variable affecting the adjacent temperatures reduction. • Maximum reduction of 0.63 °C in summer and 0.54 °C in winter at 1 m from the south facade. • Maximum reduction of 2.3% HR in winter at 1 m from the south façade. • Reduction average of 0.9% HR in summer at 1 m from the south façade. A study has been performed to quantify the impact of a green wall temperature reduction in a continental Mediterranean climate. This paper presents the results of a monitoring campaign during the summer and winter of 2021–2022. First, a database was elaborated with information on air temperature and relative humidity at four different distances from the wall (0.25, 0.50, 0.75, and 1 m) and irradiance in the vertical plane. Secondly, a statistical analysis was carried out to determine whether there is a correlation between these variables and whether there are statistically significant differences between them at different distances from the wall. Results show a strong positive correlation between air temperature recorded by sensors and the ambient air temperature for all seasons and between air temperature and relative humidity in summer. The ANOVA shows that the green wall reduces the temperature and relative humidity with statistical significance for most of the cases. These results confirmed the great capacity of green walls to improve the urban microclimate, especially during summer, by reducing air temperatures, providing evidence that greening the building envelope can complement conventional city green infrastructure and provide multiple ecosystem services to dense urban contexts. [ABSTRACT FROM AUTHOR]

This paper aims to explore gender differences in thermal comfort under coupled environmental factors, as previous studies have primarily focused on air temperature alone. The experiment involved 27 subjects, 13 males and 14 females, who experienced 12 different combinations of air temperature and air velocity environmental conditions. The subjective responses and physiological parameters were collected and analyzed. In colder environments, especially when low air temperatures are coupled with elevated air velocity, significant gender differences are observed, as females feel colder, more uncomfortable, and perceive stronger drafts than males. In hotter environments, when high air temperatures are coupled with still air, males tend to feel more uncomfortable and prefer higher air velocity, compared to females. However, few gender differences are observed when high air temperatures are coupled with elevated air velocity. Under colder conditions, forearm temperatures show a stronger correlation with subjective responses, while under hotter conditions, facial skin temperatures demonstrate a stronger correlation, for both genders. Moreover, the Elevated Air Speed Model in ASHRAE 55–2020 is found to overestimates the cooling effect of elevated air velocity for both genders. The findings suggest that gender differences should be properly considered when designing indoor thermal environments, particularly under colder conditions. [ABSTRACT FROM AUTHOR]

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

Providing the occupants with tailored feedback on their energy consumptions can be an effective method to help the users to adopt energy-efficient behaviours. However, in specific contexts such as the social housing, this may be challenging, due to the peculiarities and the economic constraints of the end-users, which make them vulnerable and needing a proper design of the information. Nevertheless, there is a lack of detailed studies in the literature analysing the effects of personalized energy feedback on the space heating consumption and the indoor air temperatures in the social housing sector. This paper aims to evaluate the effects on space heating consumption and indoor air temperature of a personalized feedback campaign delivered in three social housing buildings consisting of 27 apartments in Mediterranean climate. To this end, a personalized feedback campaign was designed and developed, providing for the use of disaggregated energy consumption indicators to show the location of excessive thermal energy consumptions. The daily thermal energy consumptions, as well as the indoor air temperatures of the single dwellings, were monitored daily for over four heating seasons and their variations were analysed before and after the implementation of the personalized feedback. A saving in normalized natural gas consumption of the buildings was found on average equal to −14,8% after the feedback campaign, with no significant variations in the indoor air temperatures. However, some critical issues have been highlighted in the response of individual end-users to the feedback provided. A greater propensity of the occupants to regulate the indoor air temperature through the thermostatic radiator valves in the unoccupied rooms of the house was also observed after the occurrence of the feedback campaign. [ABSTRACT FROM AUTHOR]

Optimizing efficiency in the operation of the HVAC system of existing buildings requires the construction of a thermal dynamic model of the building, which may be challenging because architectural metadata may be missing or obsolete. Based on a suitable set of measured data, this paper presents a novel practical methodology to create and automatically derive thermal models of existing buildings in high-fidelity simulators for energy management. To this end, the philosophy of grey-box strategies is followed to simplify the modeling and avoid the requirement of architectural metadata, facilitating and expediting the process. First, a building model with a highly reduced number of parameters is constructed by exploiting the existing similarities in the materials of the buildings and simplifying their elements to a simple one-layer parameterization. Then, the parameters of the derived model are iteratively updated while minimizing the error between the real temperature evolution and that generated by the model being identified. For this purpose, data of the room air temperature, estimated occupancy, weather conditions, and variables of the HVAC system are assumed to be available in suitable zones of the building to apply the creation and identification processes of the model, allowing that a whole digital twin of the building is constructed. The methodology is presented by its application to a real case study: the Nimbus Research Centre building at Munster Technological University, located in Cork (Ireland). The high-fidelity simulator software TRNSYS is used for the modeling task, together with the GenOpt optimization program. The results demonstrate that the proposed methodology yields a highly accurate model of the building, capable of representing reality with RMSE values consistently below 0.6 C ∘ during open-loop validation periods of up to four days. The findings suggest that this methodology may outperform other modeling techniques reported in the literature. Importantly, the proposed technique is less complex and time-consuming to implement than many of the alternatives. • Thermal modeling of existing buildings avoiding the use of architectural metadata. • Novel integration of grey-box modeling methods with high-fidelity simulators. • The proposed method copes with the drawbacks of the models found in the literature. • Methodology validated with experimental data from the Nimbus Centre (Ireland). • Results show high accuracy of greatly simplified models—RMSE<0.6 °C in 4-day horizons. [ABSTRACT FROM AUTHOR]

• The TPAM algorithm can predict the outdoor air temperature using one parameter. • The prediction accuracy is between 71.9% and 95.6%, with the average more than 80.0%. • Skipping time window can effectively keep the temperature's seasonal characteristics. Accurate prediction of outdoor air temperature is of great significance to improve the energy efficiency of HVAC system and even the whole building. The aim of this paper is to present the development and evaluation of a new algorithm based on pattern approximate matching for the prediction of outdoor air temperature using acquired historical data of five cities in China (Beijing, Harbin, Shanghai, Guangzhou and Kunming) representing different climatic regions. The optimum parameters of the algorithm for each city has been identified by its historical data. The efficiency of the prediction is validated by comparing predicted and measured outdoor air temperature. Furthermore, statistical metrics such as absolute mean error (AME), root mean square error (RMSE), and accuracy were used to evaluate the performance of the algorithm. These metrics confirm the algorithm proposed in this paper is suitable for all the five Chinese cities. All the work has been contacted using Python programming environment. [ABSTRACT FROM AUTHOR]

• Selected the outdoor design conditions based on simultaneous occurrence probability. • Analyzed the dependence relation between three weather elements using vine copula. • Modeled the multivariate joint distributions of three weather elements. • The indoor thermal risk levels of outdoor design conditions were evaluated. In building design, the commonly used outdoor weather conditions recommended by ASHRAE are generated by the annual cumulative frequency of occurrence method. This may result in air-conditioning equipment capacity being overestimated. This is because the air temperature, humidity and solar radiation that have the greatest impact on indoor peak cooling loads are selected independently. It means that the probability of these three weather parameters occurring at the same time is extremely small. This paper proposes an approach for selecting the design weather data on the basis of the exact joint distribution with specific simultaneous occurrence probabilities of these three weather parameters. For this purpose, a novel statistical method vine copula is applied to construct the theoretical multivariate joint distributions of these three weather parameters for 24-hour data and combined the design parameters of each hour with a specific simultaneous occurrence probability into a design day. The hourly meteorological records over 10 years in Tokyo are used to generate the outdoor design conditions. The results indicate that the vine copula method can create an effective theoretical model and all the design parameters selected by the simultaneous occurrence probability are significantly below that of the same conventional exceeding probability. For simultaneous occurrence probabilities of 0.4 %, 1 % and 2 %, the corresponding indoor thermal risk levels are 0.46 %, 0.52 % and 0.84 %, respectively. Therefore, the proposed method can be used as a reference for designers to determine a more reasonable capacity of air-conditioning systems. [ABSTRACT FROM AUTHOR]

• A framework is developed to assess overheating in existing building in whole summer. • Calibration simulation model is used using field measurements of indoor temperature. • Overheating risk in existing school building is assessed. • Climate change effect is studied using current and future extreme RSWY years. • Effective passive mitigation measures are provided for future extreme climates. Canadian buildings have been primarily designed to withstand cold and long winters, not hot summers. With climate change and the increase in the intensity and severity of heatwaves, it has become important to investigate overheating in buildings. However, there are limited studies on assessing and mitigating the overheating risk in existing buildings that house vulnerable populations in cold climates, especially in Canada. This paper provides a framework for the systematic assessment of overheating risks and the development of passive mitigation strategies to reduce the overheating risks without increasing cooling energy consumption under current and future climates with simulation models that are calibrated based on measured indoor air temperatures and outdoor weather conditions. The framework is applied to a school building built in 1958. The calibrated building model achieved an RMSE of less than 0.6 °C compared with measurements, a Maximum-Absolute-Difference of less than 1.9 °C, and a 1 °C Percentage-Error of less than 10 %. The simulation results from the calibrated model predicted 110 overheating hours during the year 2020. The use of exterior blind roll or a combination of night cooling and other mitigation measures that reduce solar heat gain can achieve acceptable thermal conditions. In 2044 (the future extreme midterm year), night cooling with the exterior blind roll shading would be required during extreme heat events. Whereas during 2090 (the future long-term extreme year), additional mitigation measures such as a cool roof may be required to achieve an acceptable level of thermal conditions in the school. [ABSTRACT FROM AUTHOR]

An automated methodology that generates sufficiently accurate building models is essential for both the adoption of advanced control strategies like Model Predictive Control, and the estimation of building energy flexibility required for building-grid interaction. This paper presents such a methodology for the generation of multi-zone resistance-capacitance (RC) thermal network models of residential buildings. The models are calibrated using smart thermostat data and predict the indoor air temperature up to 24 h ahead. The methodology starts with a very simple model and iteratively adds one parameter at a time; the parameter that increases the quality of the model the most, as measured with the Bayesian Information Criterion. When the quality of the model cannot be improved any further, the procedure is reversed to delete any redundant parameters. The algorithm concludes when neither adding nor removing a parameter increases the model quality. The algorithm is applied on measured data from an experimental facility, a bungalow house in Québec, Canada. The resulting model can accurately predict all 9 zone temperatures 24 h in advance with an RMSE of 0.44 °C. An examination of the estimated parameters shows that they reflect the layout of the house, previously unknown to the methodology. [ABSTRACT FROM AUTHOR]

• Two model buildings are specially built for comparative study. • Dynamic monitoring of external insulation for improving thermal environment and energy saving during heating. • The time for EPS building to approach the air temperature set value has no obvious advantage. • The energy saving effect of short-time heating operation of EPS building is very weak. • A dynamic index is proposed to evaluate the improvement effect of external insulation on indoor thermal environment. In this paper, two identical model buildings were built, one of which adopted 38 mm Expanded Polystyrene (EPS) board as the external insulation. Under the same heating set-point temperature, the dynamic change process of indoor thermal environment and heating energy consumption of EPS insulation building and non-insulation Reference building was compared and monitored. Results show that under the same heating set-point temperature, the indoor temperature of the two buildings can quickly approach the set value in about 20 min. However, the temperature rise of the internal surface of the wall lags behind. After several hours, the advantage of the temperature rise of the internal surface of the EPS building gradually appeared. The energy consumption of the two buildings is similar in the initial heating period of 20 min, and the energy saving rate of EPS building increases slowly with the increase of the heating duration. The energy saving rate is about 1%–26% in the range of 0.5–8 h intermittent heating in hot summer and cold winter zone. The change curve of cumulative temperature rise ratio with heating time is proposed to evaluate the dynamic improvement effect of external insulation on indoor thermal environment. This research provides a reference basis for the development of energy conservation. [ABSTRACT FROM AUTHOR]

The vehicle HVAC (heating, ventilating, and air conditioning) system aims to keep passengers thermal comfort and reduce energy consumption. One effective measure is to use temperature prediction to optimize HVAC systems. This paper aimed to comprehensively investigate the potential of machine learning methods applied to cockpit temperature prediction and proposed a deep learning method considering the Spatio-temporal correlation of the temperature field. Specifically, this study constructs a topological description of the temperature field inspired by the spatial and temporal correlations revealed by the Navier-Stokes equations. The Graph Convolutional Network (GCN) is used to capture the topological structure to obtain spatial features, and the Gated Recurrent Unit (GRU) is used to capture the dynamic change of node attribute to obtain temporal features. Finally, the GCGRU model can extract Spatio-temporal features of the temperature field. The experimental results show that the prediction method using Spatio-temporal features for the temperature field is feasible. The prediction performance is better than all the baseline methods and has the robustness to the data noise. This work is enlightening and may have a further reference to the feasibility study of the vehicle cabin air temperature prediction model. [ABSTRACT FROM AUTHOR]

The evaporative cooling technology (ECT) has been regarded as an effective alternative to conventional vapour compression systems due to its energy saving and eco-friendly features. Moreover, when integrating with a solid desiccant unit, ECT can further extend its application in hot and humid environments. This paper proposed and evaluated a solar-assisted solid desiccant Maisotsenko cycle (M−cycle) based indirect evaporative cooling (IEC) system with different recirculation air ratios. Three other cooling systems, including stand-alone Direct Evaporative Cooling (DEC), stand-alone M−cycle IEC, and conventional solid desiccant evaporative cooling system (SD-DEC), were also simulated and compared with the proposed system under various ambient conditions. These systems were assessed in terms of supply air temperature, indoor relative humidity ratio, water consumption rate, coefficient of performance (COP) and solar fraction in this study. A case study of the proposed system was carried out under two typical Australian climates, Darwin and Melbourne. The results indicated that the solar-assisted solid desiccant cooling system integrated with M−cycle IEC had the best cooling performance compared with the other three systems, particularly in hot and humid areas. When increasing the recirculation air ratios from 0% to 60%, a maximum reduction of 23.1% and 41.9% could be observed in the system's supply air temperature and humidity ratio, respectively. The highest COP was 0.89 when the inlet air temperature was 30 °C, and the COP value would vary as the inlet air temperature and humidity ratio increased. However, a higher water consumption rate was also observed, 31.36% to 57.94% more water would be consumed. This indicates that it requires selecting a proper recirculation air ratio for different climates to achieve the best performance. [ABSTRACT FROM AUTHOR]

• The distributions of temperature and air velocity for a curved double-skin façade (CDSF) were investigated. • The nonuniformity of solar irradiation on a curved surface is higher than that of a planar surface. • The thermal pressures of a CDSF show different values at different positions. • Solar irradiances show a power function relationship with the air velocity. • There should be transverse flows and heat transfers in the cavity of a CDSF. Curved architectural forms are becoming popular in modern building designs, resulting in more demanding requirements of curved double-skin façades (CDSF). However, CDSF's natural ventilation performance is barely known, as most current studies investigate planar DSF (PDSF). This paper experimentally evaluated the thermal performance of a reduced scale CDSF model. Experimental results show that CDSF can form natural ventilation by buoyancy, however, with significant discrepancies in the distribution of solar irradiances on the curved surface. Observed nonuniformity shows 30.05 % on average vertically and 9.12 % on average horizontally. The thermal pressures vary at different horizontal positions with a fluctuation range between 0.056 Pa and 0.063 Pa, indicating the influence of gradients and the existence of transverse flows and heat transfers in the cavity. Moreover, the effects of solar irradiation and outdoor air temperature on the CDSF are discussed. The results show that solar irradiation is the main influencing factor on the thermal performance of a CDSF, which are correlated with air velocities in the form of power functions. The influence of solar irradiation on CDSF is similar to that of PDSFs, but CDSF exhibits a bigger exponent. In comparison, outdoor air temperature shows very limited effects, and the influence of outdoor air temperature on CDSF is similar to that of PDSF. For the analyzed CDSF, the thermal pressures only fluctuate from 0.053 Pa to 0.055 Pa when the outdoor air temperature raises from 24 °C to 36 °C. [ABSTRACT FROM AUTHOR]

• Measured data quantifies savings from OCC and sequence of operations improvements. • Energy model used to parse out savings contributions of each control intervention. • Occupancy-based ventilation ineffective without supply air temperature reset. • Use of an economizer high limit returns marginal cooling savings with little effort. • Use of OCC magnifies savings from the changes to control sequences considered. Partial occupancy of commercial offices has become the norm in the wake of the COVID-19 pandemic. Given this, occupant-centric control (OCC), which adapt building systems based on occupants' presence or preferences, offer an alternative to traditional control that assumes full occupancy. However, poor sequences of operation can degrade the benefits of OCC. This paper explores this interaction by examining energy data from two buildings – one with two control logic faults corrected and an occupancy-based ventilation OCC implemented in 2020, and one with traditional ventilation – from 2019 to 2020. Sequences that impacted implementation in the first building are discussed. Then, a calibrated energy model of the second building is developed to evaluate how occupancy-based ventilation alongside changes to the sequences of operation – namely supply air temperature (SAT) reset and economizer high limits – impacted energy use. The inclusion of OCC and improved sequences in the second building saved 30.6% and 9.6% of annual heating and cooling energy, respectively. Without an SAT reset, OCC saved 4.4% and 3.9% of heating and cooling, respectively, compared to 15.7% and 5.7% when an SAT was present. These results begin to characterize the relationship sequences of operation and OCC implementations have with one another in commercial offices. [ABSTRACT FROM AUTHOR]

[Display omitted] Green spaces and vegetation cover offer various environmental benefits, including building energy-saving. Enriching the urban vegetation is fundamental in urban heat island (UHI) mitigation policies promoted over the last years in many large North American cities. For example, the Greater Toronto Area (GTA) has been promoting significant increases in urban greenery by intensifying the urban trees and green roofs. In this paper, an updated version of the open-source code of the Urban Weather Generator was developed and validated to assess the effect of the increased horizontal and vertical green infrastructure on the microclimatic thermal performance of three urban typologies within the GTA. The updates of the model efficiently estimate the cooling and warming effects of the urban greenery cover on the microclimate thermal environment. The proposed enhancements included intensifying the tree canopy and incorporating the vegetated façades and green roofs. The validation ensured the model's ability to predict the cooling and warming effects of the green infrastructure. The results confirmed that tree canopy and green façades were the most effective mitigation strategies for reducing the canyon air temperature and building energy consumption, respectively. A parametric analysis utilizing a multi-objective algorithm was also designed to evaluate the effectiveness and affordability of the applied strategies. The results showed that the ambient peak air temperature, the outdoor heat stress index, and building energy savings were reduced by up to 4.6 ˚C, 5.4, and 42.4%, respectively. Considering reducing the green infrastructure cost by 50%, the parametric study resulted in a reduction of up to 3 ˚C, 2.9, and 35.6% in the ambient peak air temperature, the outdoor heat stress index, and building energy savings, respectively. [ABSTRACT FROM AUTHOR]

Many New Zealand residential homes suffer from poor indoor air quality (IAQ). Cold, damp and mould-ridden homes can cause severe respiratory health problems. Poor IAQ can arise due to inadequate insulation and ventilation and is compounded when dwellers cannot afford to heat their homes during colder months. Grandfathering of standards means many New Zealand houses have single glazed windows and insufficient or no wall insulation. These have become mandatory for newly built houses since 2016 under the New Zealand Standard NZS4246:2016 Energy Efficiency. This paper aims to present the results of modified ultraviolet germicidal irradiation (UVGI) lights-dehumidifier coupled with two different types of filters (carbon-fibre or polyester). The modified devices were designed to improve ambient air temperature by removing moisture, particulate matter and reducing mould and bacteria growth in a room. Seven different scenarios were investigated, which include combinations of UV light and filtration. The results showed that the UVGI was superior to filtration only; however, the polyester filter with UV lights (PFUV) was the most effective configuration. The PFUV effectively impeded the mould growth, with a 13.3% reduction in relative humidity and a 4.1 °C increase in the room temperature in the absence of a heating source. The results showed good potential for improving the IAQ of buildings. [ABSTRACT FROM AUTHOR]

• An office was instrumented to collect human-activity and environmental parameters. • The ability of three models to estimate the occupancy state through the sensors data was verified. • Law of Total Probability, Naïve Bayes classifier, and CART were the chosen models. • Models were performed by considering 34 different combinations of parameters. • The number and the type of the used parameters influenced the models accuracies. Occupancy modeling in office buildings is still in progress and needs to be developed by using observable data. In this paper, an office building under the Mediterranean climate was instrumental in the collection of both indoor environmental parameters (air temperature and relative humidity, CO 2 , VOC) and user action-related variables (electricity power, window, door state, and air conditioning use). Each parameter was monitored along with the occupancy state at a one-minute time step for two years. The purpose of the investigation was to evaluate the potential application of three straightforward models, such as the Law of Total Probability (LTP), Naïve Bayes classifier (NB), and Classification and Regression Tree (CART), to estimate the occupancy state using the indoor measurements. Thirty-four (34) different combinations of parameters were applied on the developed models; the true positive rate (TPR), true negative rate (TNR), and accuracy were used as evaluation metrics. The results confirmed that the performances of the models were influenced by both the number and the typology of the used parameters. In particular, the CART model was found to be the least affected by them; almost half of the parameters' combinations provided accuracies higher than 93% and TNR higher than TPR. Accuracies of the order of 90% were obtained with NB and LTP. [ABSTRACT FROM AUTHOR]