A (semi-)closed greenhouse is a novel greenhouse with an active cooling system and temporary heat storage in an aquifer. Air is cooled, heated and dehumidified by air treatment units. Climate in (semi-)closed greenhouses differs from that of conventional open greenhouses. The aims of our research were first, to analyze the effect of active cooling on greenhouse climate, in terms of stability, gradient and average levels; second, to determine crop growth and production in closed and semi-closed greenhouses. An experiment with tomato crop was conducted from December 2007 until November 2008 in a closed greenhouse with 700 W m-2 cooling capacity, two semi-closed greenhouses with 350 and 150 W m-2 cooling capacity, respectively, and an open greenhouse. The higher the cooling capacity, the more independent the greenhouse climate was of the outside climate. As the cooling ducts were placed underneath the plants, cooling led to a remarkable vertical temperature gradient. Under sunny conditions temperature could be 5°C higher at the top than at the bottom of the canopy in the closed greenhouse. Cumulative production in the semi-closed greenhouses with 350 and 150 W m-2 cooling capacity were 10% (61 kg m-2) and 6% (59 kg m-2) higher than that in the open greenhouse (55 kg m-2), respectively. Cumulative production in the closed greenhouse was 14% higher than in the open greenhouse in week 29 after planting but at the end of the experiment the cumulative increase was only 4% due to botrytis. Model calculations showed that the production increase in the closed and semi-closed greenhouses was explained by higher CO2 concentration.
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Semi-closed greenhouses have been developed in which window ventilation is minimized due to active cooling, enabling enhanced CO2 concentrations at high irradiance. Cooled and dehumidified air is blown into the greenhouse from below or above the canopy. Cooling below the canopy may induce vertical temperature gradients along the length of the plants. Our first aim was to analyze the effect of the positioning of the inlet of cooled and dehumidified air on the magnitudes of vertical temperature and VPD gradients in the semi-closed greenhouses. The second aim was to investigate the effects of vertical temperature gradients on assimilate production, partitioning, and fruit growth. Tomato crops were grown year-round in four semiclosed greenhouses with cooled and dehumidified air blown into the greenhouses from below or above the crop. Cooling below the canopy induced vertical temperature and VPD gradients. The temperature at the top of the canopy was over 5°C higher than at the bottom, when outside solar radiation was high (solar radiation >250 J cm-2 h-1). Total dry matter production was not affected by the location of the cooling (4.64 and 4.80 kg m-2 with cooling from above and from below, respectively). Percentage dry matter partitioning to the fruits was 74% in both treatments. Average over the whole growing season the fresh fruit weight of the harvested fruits was not affected by the location of cooling (118 vs 112 g fruit-1). However, during summer period the average fresh fruit weight of the harvested fruits in the greenhouse with cooling from below was higher than in the greenhouse with cooling from above (124 vs 115 g fruit-1).
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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 carbon footprint for the downstream dairy value chain, milk collection and dairy processing plants was estimated through the contribution of emissions per unit of collected and processed milk, whereas that for the upstream dairy value chain, input supply and production was not considered. A survey was conducted among 28 milk collectors and four employees of processing plants. Two clusters were established: small- and large-scale milk collectors. The means of carbon dioxide equivalent per kilogramme (CO2-eq/kg) milk were compared between clusters by using independent sample t-test. The average utilisation efficiency of milk cooling refrigerators for small- and large-scale collectors was 48.5 and 9.3%, respectively. Milk collectors released carbon footprint from their collection, cooling and distribution practices. The mean kg CO2-eq/kg milk was 0.023 for large-scale collectors and 0.106 for small-scale collectors (p < 0.05). Milk processors contributed on average 0.37 kg CO2-eq/kg milk from fuel (diesel and petrol) and 0.055 from electricity. Almi fresh milk and milk products processing centre emitted the highest carbon footprint (0.212 kg CO2-eq/kg milk), mainly because of fuel use. Generally, in Ziway-Hawassa milk shed small-scale collectors released higher CO2-eq/kg milk than large-scale collectors.
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Renewable energy sources have an intermittent character that does not necessarily match energy demand. Such imbalances tend to increase system cost as they require mitigation measures and this is undesirable when available resources should be focused on increasing renewable energy supply. Matching supply and demand should therefore be inherent to early stages of system design, to avoid mismatch costs to the greatest extent possible and we need guidelines for that. This paper delivers such guidelines by exploring design of hybrid wind and solar energy and unusual large solar installation angles. The hybrid wind and solar energy supply and energy demand is studied with an analytical analysis of average monthly energy yields in The Netherlands, Spain and Britain, capacity factor statistics and a dynamic energy supply simulation. The analytical focus in this paper differs from that found in literature, where analyses entirely rely on simulations. Additionally, the seasonal energy yield profile of solar energy at large installation angles is studied with the web application PVGIS and an hourly simulation of the energy yield, based on the Perez model. In Europe, the energy yield of solar PV peaks during the summer months and the energy yield of wind turbines is highest during the winter months. As a consequence, three basic hybrid supply profiles, based on three different mix ratios of wind to solar PV, can be differentiated: a heating profile with high monthly energy yield during the winter months, a flat or baseload profile and a cooling profile with high monthly energy yield during the summer months. It is shown that the baseload profile in The Netherlands is achieved at a ratio of wind to solar energy yield and power of respectively Ew/Es = 1.7 and Pw/Ps = 0.6. The baseload ratio for Spain and Britain is comparable because of similar seasonal weather patterns, so that this baseload ratio is likely comparable for other European countries too. In addition to the seasonal benefits, the hybrid mix is also ideal for the short-term as wind and solar PV adds up to a total that has fewer energy supply flaws and peaks than with each energy source individually and it is shown that they are seldom (3%) both at rated power. This allows them to share one cable, allowing “cable pooling”, with curtailment to -for example-manage cable capacity. A dynamic simulation with the baseload mix supply and a flat demand reveals that a 100% and 75% yearly energy match cause a curtailment loss of respectively 6% and 1%. Curtailment losses of the baseload mix are thereby shown to be small. Tuning of the energy supply of solar panels separately is also possible. Compared to standard 40◦ slope in The Netherlands, facade panels have smaller yield during the summer months, but almost equal yield during the rest of the year, so that the total yield adds up to 72% of standard 40◦ slope panels. Additionally, an hourly energy yield simulation reveals that: façade (90◦) and 60◦ slope panels with an inverter rated at respectively 50% and 65% Wp, produce 95% of the maximum energy yield at that slope. The flatter seasonal yield profile of “large slope panels” together with decreased peak power fits Dutch demand and grid capacity more effectively.
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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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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 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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With increase in awareness of the risks posed by climate change and increasingly severe weather events, attention has turned to the need for urgent action. While strategies to respond to flooding and drought are well-established, the effects - and effective response - to heat waves is much less understood. As heat waves become more frequent, longer-lasting and more intense, the Cool Towns project provides cities and municipalities with the knowledge and tools to become heat resilient. The first step to developing effective heat adaptation strategies is identifying which areas in the city experience the most heat stress and who are the residents most affected. This enables decision-makers to prioritise heat adaptation measures and develop a city-wide strategy.The Urban Heat Atlas is the result of four years of research. It contains a collection of heat related maps covering more than 40,000 hectares of urban areas in ten municipalities in England, Belgium, The Netherlands, and France. The maps demonstrate how to conduct a Thermal Comfort Assessment (TCA) systematically to identify heat vulnerabilities and cooling capacity in cities to enable decision-makers to set priorities for action. The comparative analyses of the collated maps also provide a first overview of the current heat resilience state of cities in North-Western Europe.
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Background There currently is no field test available for measuring maximal exercise capacity in people with stroke. Objective To determine the feasibility, reproducibility and validity of the Shuttle Test (ST) to measure exercise capacity in people with stroke. Design Longitudinal study design. Setting Rehabilitation department, day care centres from a nursing home and private practices specialized in neuro rehabilitation. Subjects People with subacute or chronic stroke. Interventions A standardized protocol was used to determine feasibility, reproducibility and validity of the 10-meter Shuttle Test (10mST). Main measures Number of shuttles completed, 1stVentilatory Threshold (1stVT). Results The associations of the number of shuttles completed and cardiopulmonary capacity as measured with a portable gas analyser were r > 0.7, confirming good convergent validity in subacute and chronic people with stroke. Criterion validity, however, indicates it is not a valid test for measuring maximal cardiopulmonary capacity (VO2max). Only 60% of participants were able to reach the 1stVT. Higher cardiopulmonary capacity and a higher total score of the lower extremity Motricity Index contributed significantly to a higher number of shuttles walked (p = 0.001). Conclusions The Shuttle Test may be a safe and useful exercise test for people after stroke, but may not be appropriate for use with people who walk slower than 2 km/h or 0.56 m/s.
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