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Living environment, heating-cooling behaviours and well-being

At a time when the population is ageing and most people choose to live in their own home for as long as possible, it is important to consider various aspects of supportive and comfortable environments for housing. This study, conducted in South Australia, aims to provide information about the links between the type of housing in which older people live, the weather and occupants’ heating and cooling behaviours as well as their health and well-being. The study used a Computer-Assisted Telephone Interviewing (CATI) system to survey 250 people aged 65 years and over who lived in their own home. The respondents were recruited from three regions representing the three climate zones in South Australia: semi-arid, warm temperate and temperate. The results show that while the majority of respondents reported being in good health, many lived in dwellings with minimal shading and no wall insulation and appeared to rely on the use of heaters and coolers to achieve thermally comfortable conditions. Concerns over the cost of heating and cooling were shared among the majority of respondents and particularly among people with low incomes. Findings from this study highlight the importance of providing information to older people, carers, designers and policy makers about the interrelationships between weather, housing design, heating and cooling behaviours, thermal comfort, energy use and health and well-being, in order to support older people to age in place independently and healthily. https://doi.org/10.1016/j.buildenv.2019.03.023 LinkedIn: https://www.linkedin.com/in/jvhoof1980/

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Living environment, heating-cooling behaviours and well-being

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REALCOOL- Really cooling water bodies in cities

Small urban water bodies, such as ponds or canals, are commonly believed to solve urban heat problems but recent research shows that the cooling effect of large urban water bodies on hot summer days is quite limited and can actually induce a night-time warming effect. However, shading, vaporising water and proper natural ventilation might help to keep urban water bodies and their surroundings cooler. But how to combine these strategies in urban design?The ‘Really cooling water bodies in cities’ (REALCOOL) research project explored the most effective combinations of shading, water vaporisation and natural ventilation around small urban water bodies. Optimal cooling strategies were developed for common urban water bodies in temperate climate zones. They are now made available to designers as virtual design prototypes

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Creating prototypes for cooling urban water bodies

When addressing urban heat problems, climate- conscious urban design has been assuming that urban water bodies such as canals, ditches or ponds cool down their surroundings. Recent research shows that this is not necessarily the case and that urban water bodies may actually have a warming e ect, particularly during late summer season nights. There are however indications that water can have a cooling potential if brought together with the right shading, evaporation and ventilation strategies. Yet, it is not clear how this should be achieved. Knowledge on such spatial conﬁgurations should thus be developed and made available to design practice. This challenge is directly addressed by the “REALCOOL” project, a research aiming to deﬁne design prototypes showing the physical processes behind the e ective cooling potential of urban water bodies, that design professionals can take as conceptual design frameworks.

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Cooling Urban Water Environments: Design Prototypes for Design Professionals

This paper presents five design prototypes for cool urban water environments developed in the 'Really cooling water bodies in cities' (REALCOOL) project. The REALCOOL prototypes address an urgent need: urban water bodies, such as ponds or canals, are often assumed to cool down their surroundings during days with heat stress, whereas recent research shows that this is not always the case and that urban water bodies may actually have warming effects too. There are, however, indications that shading, vaporising water, and proper ventilation can keep water bodies and their surroundings cooler. Yet, it is necessary to explore how these strategies can be optimally combined and how the resulting design guidelines can be communicated to design professionals. The REALCOOL prototypes communicate the spatial layout and biometeorological effects of such combinations and assist design decisions dealing with urban water environments. The micrometeorological simulations with Envimet showed that the prototypes led to local reductions on daytime PET from 1 °C to 7 °C, upon introducing shade. Water mist and fountains were also cooling solutions. The important role of ventilation was confirmed. The paper discusses and concludes about the use of the prototypes as tools for urban design practice.

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Cooling Urban Water Environments: Design Prototypes for Design Professionals

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To what extent are urban water bodies really cooling?

Urban water bodies like ponds or canals are commonly assumed to provide effective cooling in hot periods. Some of the evidence that feeds this assertion is based on remote sensing observations at relatively large scales. Such observations generally reveal reduced surface temperatures of water bodies during daytime, relative to their urbanized environment. This is to be expected because of the extremely large heat capacity of water in combination with its ability to transport heat away from the water surface by turbulent mixing. However, this also implies that the cooling of a water body may proceed only slowly, which may result in higher night-time surface temperatures. This can lead to water bodies contributing to night-time urban heat islands. The existence of a surface-air temperature gradient is a necessary, but insufficient condition for water bodies to influence their environment. In order to noticeably affect the atmospheric temperature, the cooler or warmer air near the water surface needs to be transported to the urban surroundings. Furthermore, for humans such effects are generally only relevant if they are present at a height of 1-2 m. This requires the fetch over the water to be sufficiently large, so that the internal boundary layer can grow to these atmospheric levels. Furthermore, since not only temperature but also wind (ventilation), humidity and radiation contribute to the heat load of humans, possible cooling or heating effects need to be considered in terms of physiologically meaningful quantities, such as the Physiological Equivalent Temperature (PET). Taking such considerations into account, it is no surprise that the effect of water bodies on their atmospheric surroundings are generally found to be small or even nearly absent when considering evidence from atmospheric measurements.Although there are indications that proper combinations of shading, evaporation and ventilation interventions around water bodies can help to keep their surroundings cooler during summer, it is virtually unknown how these strategies can be optimally combined in designs to counter urban heat effectively. The ‘Really cooling water bodies in cities’ (REALCOOL) project explores possible cooling effects of such combinations for relatively small urban water bodies (characteristic horizontal dimension up to a few tens of meters, maximum depth 3m). The goal is to create evidence-based design guidelines of cooling urban water environments — design prototypes — meant for application in urban and landscape design practice.This presentation will address the cooling effects of the design prototypes evaluated with micrometeorological simulations. Special attention will be paid to the cooling effects of the water bodies in the designs. These were assessed using ENVI_MET version 4.1.3., which allows the user to choose the intensity of turbulent mixing of the water. Comparisons with observations and results from water temperature simulations with a model that assumes perfectly mixed water (the “Cool Water Tool”, CWT) showed that enhancing the turbulent mixing in ENVI_MET strongly improves water temperature simulations. Three design experiments were implemented in ENVI_MET: Exp1) testbeds, which are spatial reference situations derived from an inventory of common urban water bodies in The Netherlands, characterized by the shape and dimensions of the water body and the type of urban environment; Exp2) testbeds in which the area occupied by the water was replaced with the paving materials or vegetation flanking the water body in the original testbed; Exp3) design options with optimal combinations of shading, evaporation and ventilation. All simulations were performed for the same set of meteorological conditions, representing a typical heatwave day in The Netherlands. The initial water temperature depends on the water depth and was determined from simulations with the CWT, run for the same heatwave day repetitively until a quasi-equilibrium state was reached.Model outcomes from ENVI_MET were evaluated for the normally warmest period during daytime (around 15:00 CET) and the coolest period during night-time (around 5:00 CET) in the summer, using water temperature just below the water surface and using air temperature and PET at a height of 1.5m. The cooling effect is defined as the difference in air temperature and PET, respectively, between the different design experiments. The differences were computed from the spatial averages over two areas: the area directly above the water surface (Exp1, Exp3) or its replacement (Exp2) and the area directly bordering the water (like quays and sidewalks, called “pedestrian area” hereafter).The simulations with ENVI_MET suggest that the cooling effect of small water bodies on the air temperature is quite small and often negligible (Exp1-Exp2). This is also true for the optimized designs (Exp3-Exp2). The presence of the water body in the testbeds reduced the daytime air temperature in the afternoon by at most 0.8°C directly over the water body and 0.6°C in the pedestrian area (Exp1-Exp2). PET was reduced by at most 1.8°C and 1.9°C, respectively. During night-time, there was a very slight warming effect in a majority of cases, of at most 0.3°C in air temperature. Warming effects in terms of PET were even smaller. The optimized designs led to a reduction of water temperature of at best 0.5°C, relative to the reference situations (Exp1-Exp3). Air temperature was reduced by at most 0.8°C, relative to the temperature in original testbeds. The Physiological Equivalent Temperature (PET) could be reduced by as much as 7°C at 15:00 CET, but this difference was mainly due to shading effects of trees, not to the presence of water.We conclude that small urban water bodies like the ones tested here may not be the most relevant adaptation measure to create cooler urban environments. Their size may simply be too small to have meaningful thermal effects in their surroundings, in accordance with micrometeorological theory on the development of internal boundary layers. Only for water bodies that are sufficiently large cooling effects may become noticeable. This is then also true for possible warming effects. However, the openness of urban water bodies and their surroundings allows ventilation and provides room for trees that provide shade. The combination of these aspects which both lead to cooling effects was found to dominate favourable changes in daytime PET in particular.

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The Living Environment and Thermal Behaviours of Older South Australians

Ageing brings about physiological changes that affect people’s thermal sensitivity and thermoregulation. The majority of older Australians prefer to age in place and modifications to the home environment are often required to accommodate the occupants as they age and possibly become frail. However, modifications to aid thermal comfort are not always considered. Using a qualitative approach this study aims to understand the thermal qualities of the existing living environment of older South Australians, their strategies for keeping cool in hot weather and warm in cold weather and to identify existing problems related to planning and house design, and the use of heating and cooling. Data were gathered via seven focus group sessions with 49 older people living in three climate zones in South Australia. The sessions yielded four main themes, namely ‘personal factors’, ‘feeling’, ‘knowing’ and ‘doing’. These themes can be used as a basis to develop information and guidelines for older people in dealing with hot and cold weather. Original publication at MDPI: https://doi.org/10.3390/ijerph16060935 © 2018 by the authors. Licensee MDPI.

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The Living Environment and Thermal Behaviours of Older South Australians

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Building and building systems energy prediction models to enhance energy flexibility control

The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.

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Building and building systems energy prediction models to enhance energy flexibility control

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A belief-desire-intention agent for modeling building managers’ air-conditioning setpoint adjustment behavior under demand response

Agent-based modeling (ABM) is a widely used method for evaluating demand response (DR) strategies. To comprehensively assess the impact of DR strategies on a district cooling system, the integration of building managers’ DR behavior is essential. However, most ABM studies focus on technical optimization while overlooking the behavioral factors that may exist in building managers’ decision-making processes. To address this gap, this paper introduces an agent-based model using the belief-desire-intention (BDI) framework to simulate building managers’ air-conditioning setpoint adjustment behavior under DR, integrating the reasoning capabilities and irrational behavior factors.

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Living comfortably with heat in Australia – preferred indoor temperatures and climate zones

While the optimal mean annual temperature for people and nations is said to be between 13 °C and 18 °C, many people live productive lives in regions or countries that commonly exceed this temperature range. One such country is Australia. We carried out an Australia-wide online survey using a structured questionnaire to investigate what temperature people in Australia prefer, both in terms of the local climate and within their homes. More than half of the 1665 respondents (58%) lived in their preferred climatic zone with 60% of respondents preferring a warm climate. Those living in Australia's cool climate zones least preferred that climate. A large majority (83%) were able to reach a comfortable temperature at home with 85% using air-conditioning for cooling. The preferred temperature setting for the air-conditioning devices was 21.7 °C (SD: 2.6 °C). Higher temperature set-points were associated with age, heat tolerance and location. The frequency of air-conditioning use did not depend on the location but rather on a range of other socio-economic factors including having children in the household, the building type, heat stress and heat tolerance. We discuss the role of heat acclimatisation and impacts of increasing air-conditioning use on energy consumption.

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