Thermal comfort in dwellings in the subtropical highlands - Case study in the Ecuadorian Andes

Thermal comfort in dwellings located in different weather conditions have been largely studied. The indoor environmental criteria have been well defined for mechanically conditioned buildings in mid-latitudes and naturally ventilated spaces in the subtropics. The subtropics are known for being hot-humid environments at low altitude. However, the highlands in the tropics have a subtropical-highland climate characterised by narrow annual temperature oscillation, noticeable diurnal temperature variation and high levels of solar radiation and precipitation due to its latitude and altitude. Field thermal comfort studies in housing in the Highlands reveals up to a 90% of user's satisfaction to temperature below 18°C. Indoor temperatures in dwellings in the Andes highlands can be even lower than 18°C as buildings are uninsulated and operate under free-running conditions throughout the year. This study seeks to identify the thermal comfort range and the difference in residents' perception of inhabitants living between 2300 m and 3100 m above sea level, in the Andes highlands. 195 thermal comfort votes were collected during the dry season. Results show that people living in the high-altitude are more sensitive to draught and prefer lower temperatures (16°C - 24°C), while inhabitants living in the low-altitude find temperatures above 26°C pleasant and prefer higher air movement

Evaluation of the new Design Summer Year weather data using parametrical buildings

The Charted Institution of Building Services Engineers (CIBSE) updated the near extreme weather (Design Summer Year – DSY) for all 14 locations in the UK in 2016. This new release attempts to address the underlying shortcomings of the previous definition where the averaged dry bulb temperature was the sole metric to choose DSY among source weather years. The aim of this research is to evaluate whether the new definition of the probabilistic DSYs can consistently represent near extreme condition. London historical weather data and their correspondent DSYs were used in this research. Dynamic thermal modelling using EnergyPlus was carried out on large number single zone offices (parametric study) which represent a large portion of cellular offices in the UK. The predicted indoor warmth from the sample building models show that these new definitions are not always able to represent near extreme conditions. Using multiple years as DSY is able to capture different types of summer warmth but how to use one or all of these DSYs to make informed judgement on overheating is rather challenging. The recommended practice from this research is to use more warm years for the evaluation of overheating and choose the near extreme weather from the predicted indoor warmt

A bootstrap method to investigate the variability of overheating risk against the future climate uncertainty in dwellings

Future overheating risk in dwellings can be potentially mitigated by minimising the variability of overheating hours against uncertainties in future climate via robust optimisation. However, the estimation of this variability value through the utilisation of percentile-based probabilistic weather data has yet to be sufficiently investigated. In this simulation-based study, the bootstrap method is used to quantify the accuracy of the variability estimation via percentilebased weather data. The results indicate significant overheating risk in regulation-compliant houses. An increased degree of difficulty is also suggested in obtaining accurate estimations when considering time periods further in the future and when assuming higher carbon emissions. In addition, the skew normal distribution can be used for a simpler and faster estimation, but the underlying uncertainties must be strengthened throughout its implementation

Modeling of Photovoltaic-Thermal District Heating with Dual Thermal Modes

Solar photovoltaic thermal (PVT) collectors could be a competitive addition to district heating systems, particularly in areas with high energy density since they simultaneously produce electricity and heat whilst increasing the PV efficiency through cooling. This study presents a new Modelica PVT model, which is used together with EnergyPlus in a co-simulation setup to assess the technical feasibility of solar PVT district heating in new builds. The model has been applied to a block of 12 2-bedroom terraced houses with a 184m2 PVT array on the south facing side of the roof. It was identified that well-designed seasonal PVT heating configurations and control schemes are required to maximise PVT outputs. PVT dual thermal modes occur when the PV is either connected to a load or producing at close to the maximum power point. Integrating the dual modes into a control system could be more economical if heat tariffs were higher than electrical ones when heat demand is greater than the PVT thermal output

Heating Ventilating and Air-Conditioning (HVAC) equipment taxonomy

Past efforts to reduce carbon emissions from the non-domestic building sector have had limited success in the UK. One of the reasons for this is a general absence of data addressing the non-domestic building sector, leading further to a lack of transparent and validated methods for energy use benchmarks and both statistical and predictive energy use modelling. This paper addresses this issue by proposing a heating ventilating and air-conditioning (HVAC) equipment taxonomy that will allow compatibility across building sector energy modelling, benchmarking and surveying. The paper presents a comprehensive, yet easily expandable, friendly to use HVAC equipment taxonomy. The main aim of the HVAC equipment taxonomy is to assist both predictive and statistical building energy end use modelling, surveying fieldwork and analysis of all building types and the allocation of energy to end uses. The HVAC equipment taxonomy developed also includes information about equipment energy efficiency in terms of efficiency coefficients or auxiliary energy consumption for both design and part load. This is supported by a review of what are sometimes contradicting and ill-defined energy efficiency indices, especially with regard to part-load operation

Simulation-time reduction techniques for a retrofit planning tool

The design of retrofitted energy efficient buildings is a promising option towards achieving a cost-effective improvement of the overall building sector’s energy performance. With the aim of discovering the best design for a retrofitting project in an automatic manner, a decision making (or optimization) process is usually adopted, utilizing accurate building simulation models towards evaluating the candidate retrofitting scenarios. A major factor which affects the overall computational time of such a process is the simulation execution time. Since high complexity and prohibitive simulation execution time are predominantly due to the full-scale, detailed simulation, in this work, the following simulation-time reduction methodologies are evaluated with respect to accuracy and computational effort in a test building: Hierarchical clustering; Koopman modes; and Meta-models. The simplified model that would be the outcome of these approaches, can be utilized by any optimization approach to discover the best retrofitting option

Insights from inside the school walls: Contextual data crowdsourcing and feedback mechanisms for UK school stock modelling

The auto-generation of UK school building stock models could facilitate non-domestic carbon emissions tracking. However, contextual fabric and building service data are required to differentiate between asset or operational performance, and these may only be available in situ from building users. Engaging such groups through proposed data crowdsourcing would require robust feedback and data gathering mechanisms to be developed to overcome motivational and informational barriers. This paper describes five stakeholder sessions and a crowdsourcing survey of 139 responses from London schools to better understand these two mechanisms. Aesthetics and budgetary drivers were found to be persistent amongst participants, with a diversity of views on achieving these in practice. This research should inform future data gathering and develop more updated and robust stock refurbishment datasets

A decision support tool for building design: An integrated generative design, optimisation and life cycle performance approach

Building performance evaluation is generally carried out through a non-automated process, where computational models are iteratively built and simulated, and their energy demand is calculated. This study presents a computational tool that automates the generation of optimal building designs in respect of their Life Cycle Carbon Footprint (LCCF) and Life Cycle Costs (LCC). This is achieved by an integration of three computational concepts: (a) A designated space-allocation generative-design application, (b) Using building geometry as a parameter in NSGA-II optimization and (c) Life Cycle performance (embodied carbon and operational carbon, through the use of thermal simulations for LCCF and LCC calculation). Examining the generation of a two-storey terrace house building, located in London, UK, the study shows that a set of building parameters combinations that resulted with a pareto front of near-optimal buildings, in terms of LCCF and LCC, could be identified by using the tool. The study shows that 80% of the optimal building’s LCCF are related to the building operational stage (σ = 2), while 77% of the building’s LCC is related to the initial capital investment (σ = 2). Analysis further suggests that space heating is the largest contributor to the building’s emissions, while it has a relatively low impact on costs. Examining the optimal building in terms compliance requirements (the building with the best operational performance), the study demonstrated how this building performs poorly in terms of Life Cycle performance. The paper further presents an analysis of various life-cycle aspects, for example, a year-by-year performance breakdown, and an investigation into operational and embodied carbon emissions

A Comparison of the ASHRAE Secondary HVAC Toolkit Detailed and Simple Cooling Coil Models with Manufacturers' Data

Modelling a complete HVAC system requires a detailed knowledge of the system component performance. One of the main components of the HVAC system is the air handling unit (AHU), the essence of which is the cooling coil, a complex component to model. The HVAC Secondary Toolkit, developed by ASHRAE, presents two cooling and dehumidifying coil models; detailed (CCDET) and simple (CCSIM). The CCDET model is suitable for coils for which detailed geometrical data are available, whereas the CCSIM model calculates coil performance based on the coil properties at its rating point. Data necessary for determining coil geometry have been collected from several manufacturers’ catalogues. In addition, some manufacturers also publish the coil characteristics at the rating point, but these data are very limited and cover only a few coils from the whole product range. When available, these data are compared to the CCDET model outputs calculated for the same inputs stated in the manufacturer catalogue. The CCSIM model uses CCDET outputs at the rating point for coil performance calculations. The paper compares the outputs from these two models over the whole range of input variables (mass flow rates, entering temperatures and humidity ratios). Some manufacturers also provide coil selection software which calculates the coil performance at different operating conditions. This paper also compares the outputs from the CCDET coil model using manufacturers’ geometrical data with CCSIM coil model outputs, hence providing practical guidance regarding the choice of an appropriate level of modelling when carrying out AHU simulations