Using Kriging regression to improve the stability and diversity in NSGA-II

This is the author accepted manuscript. The final version is available from IBPSANon-dominated sorting genetic algorithm version 2 (NSGA-II) is a multi-objective optimisation method. NSGA-II is often used to optimise the design of building. This paper details small improvements to this algorithm using ‘fitness approximation’ methods. Fitness approximation is used speed up the conversion of NSGA-II. Radial basis functions networks have been shown to be useful for this. Although there are many types of fitness approximation function that could be use for this purpose, Kriging methods have not yet been tested. In this paper, Kriging models are compared to standard NSGA-II. The results show that Kriging-based fitness approximation slightly improves upon standard NSGAII. More work is needed to test this method on different building types as well as more complex problems, such as those associated with HVAC design.The authors would like to thank for EPSRC for funding this research [Ref: EP/M021890/1

Efficient summertime overheating analysis using decomposed weather files

This is the author accepted manuscript. The final version is available from IBPSA via the link in this record.Overheating within European buildings is a big problem and the building design plays a significant role in any health-related outcomes. We show that heatwaves can be extracted from historic data based on how they affect buildings, rather than how they affect the external temperature. We propose a simple way of rating heatwaves based on the severity of their effect on the internal environment.The authors would like to thank the Engineering and Physical Sciences Research Council (EPSRC) for their support [Grant no: EP/M021890/1]

A comparison between Gaussian Process emulation and Genetic Algorithms for optimising energy use of buildings

Computing speed has increased greatly over recent years. Building designers can now simulate complex building models in a short time. However, even with short simulation times, building optimisation routines can still take too long for some applications. In this paper, we compare how well genetic algorithms (GAs) and Gaussian process emulation with sequential optimisation (GPESO) optimise a building to minimise the energy use. The GA approach performs a GA routine on an EnergyPlus model and the GPESO technique creates a Gaussian Process emulator (GPE) also based on the EnergyPlus model. The GPESO uses an expected improvement algorithm to sequentially improve the GPE. The results show that the GPESO technique outperforms the GA in terms of minimising the number of simulations required and the solution obtained.This work was supported by the Engineering and Physical Sciences Research Council [EPSRC grant number EP/J002380/1]

The implications of transporting architecture on human health

This is the author accepted manuscript.Where modern buildings are unable to maintain the internal environment to within comfort levels they often rely on mechanical systems to become habitable. This could be due to bad design or putting the building in an environment for which it is not suited. Due to climate change it is likely that all buildings will in effect and time be moved to an environment for which it is not suited. In this work the effects of changes in climate on the internal environment will be explored and an index to define how moveable a construction might be, will be developed.The authors would like to thank the EPSRC for their support [grant ref: EP/J002380/1

Future probabilistic hot summer years for overheating risk assessments

As the 2003 Paris heatwave showed, elevated temperatures in buildings can cause thousands of deaths. This makes the assessment of overheating risk a critical exercise. Unfortunately current methods of creating example weather time series for the assessment of overheating are based on a single weather variable, and hence on only one driver of discomfort or mortality. In this study, two alternative approaches for the development of current and future weather files are presented: one (pHSY-1) is based on Weighted Cooling Degree Hours (WCDH), the other (pHSY-2) is based on Physiologically Equivalent Temperature (PET). pHSY-1 and pHSY-2 files were produced for fourteen locations. These were then compared with the existing probabilistic future Design Summer Year (pDSY) and the probabilistic future Test Reference Year. It was found that both pHSY-1 and pHSY-2 are more robust than the pDSY. It is suggested that pHSY-1 could be used for assessing the severity and occurrence of overheating, while pHSY-2 could be used for evaluating thermal discomfort or heat stress. The results also highlight an important limitation in using different metrics to compare overheating years. If the weather year is created by a ranking of a single environmental variable, to ensure consistent results assessment of the building should be with a similar single metric (e.g. hours >28 °C or WCDH), if however the weather year is based upon several environmental variables then a composite metric (e.g. PET or Fanger’s PMV) should be used. This has important implications for the suitability of weather files for thermal comfort analysis.This research was supported by Engineering and Physical Science Research Council (EPSRC) via grants EP/M021890/1 and EP/M022099/1. All data created during this research are available from the University of Bath data archive at http://doi.org/10.15125/BATH-00190

The impact of local variations in a temperate maritime climate on building energy use

This is the author accepted manuscript. The final version is available from Taylor & Francis (Routledge) via the DOI in this record.We investigate the impact of local climatic variations on the energy performance of buildings by conducting simulations using weather files generated from high-resolution weather measurements covering 33 stations within a 77km2 area in southern Vancouver Island, British Columbia, Canada. Weather files were created by resampling and cleaning the data and applying appropriate models to obtain unmeasured values. The difference in microclimate has been analysed statistically and graphically; average annual temperature varies by around 1°C, and at certain times there is a 6°C variation across the (very small) region. Building energy simulations of a small naturally-ventilated office building and a larger air-conditioned building were performed using EnergyPlus for all weather files. Significant variation is found spatially and temporally which would have substantial implications for building design and energy use. The variation in annual heating energy use is +/- 5% of the mean, equivalent to 18kWh/m2 /a, with even greater relative variation in cooling energy use.Engineering and Physical Sciences Research Council (EPSRC) - The creation of localized current and future weather for the built environment (EP/M022099/1

A revised morphing algorithm for creating future weather for building performance evaluation

This is the final version. Available from SAGE Publications via the DOI in this record. Climate change is one of the greatest challenges the building industry faces. Engineers and architects require representative future weather data if they would like to see how their buildings and designs will fare under a changing climate. The most common method used to create future weather involves manipulating observations commonly known as morphing, but the most used algorithms can create implausible weather conditions due to their unbounded nature. Here, bounded morphing algorithms will be described and their effectiveness proved mathematically. The improved bounded method applies two additional conditions on the morphed distribution to the maximum and minimum values, in addition to the mean values. The benefits over the standard approach will also be illustrated considering the changes in the distribution of temperature and solar irradiation due to climate change. The improved algorithms outperform the standard morphing procedures in terms of preserving the underlying climate signal while not creating unrealistic or implausible weather conditions. This method should give engineers confidence that the generated future weather series are more robust and representative of potential future weather. Practical application: The use of future weather to inform building design is now commonplace within the industry. Reliable weather files are crucial to support and deliver strategies for decarbonisation and adaptation to climate change in the built environment and the wider industry. This article provides support for the use of revised morphing algorithms which result in improved future weather time series which can be used in building simulation. For example, when applied to the temperature, it can be used to produce more accurate representations of future temperature profiles due to climate change, and for building performance assessment, such as energy consumption and overheating. It plays an important role in producing reliable and realistic weather data for future-proof building design.CIBSE, Innovate U

Fast variables determine the epidemic threshold in the pairwise model with an improved closure

Pairwise models are used widely to model epidemic spread on networks. These include the modelling of susceptible-infected-removed (SIR) epidemics on regular networks and extensions to SIS dynamics and contact tracing on more exotic networks exhibiting degree heterogeneity, directed and/or weighted links and clustering. However, extra features of the disease dynamics or of the network lead to an increase in system size and analytical tractability becomes problematic. Various `closures' can be used to keep the system tractable. Focusing on SIR epidemics on regular but clustered networks, we show that even for the most complex closure we can determine the epidemic threshold as an asymptotic expansion in terms of the clustering coefficient.We do this by exploiting the presence of a system of fast variables, specified by the correlation structure of the epidemic, whose steady state determines the epidemic threshold. While we do not find the steady state analytically, we create an elegant asymptotic expansion of it. We validate this new threshold by comparing it to the numerical solution of the full system and find excellent agreement over a wide range of values of the clustering coefficient, transmission rate and average degree of the network. The technique carries over to pairwise models with other closures [1] and we note that the epidemic threshold will be model dependent. This emphasises the importance of model choice when dealing with realistic outbreaks

Mapping structural diversity in networks sharing a given degree distribution and global clustering: Adaptive resolution grid search evolution with Diophantine equation-based mutations

Methods that generate networks sharing a given degree distribution and global clustering can induce changes in structural properties other than that controlled for. Diversity in structural properties, in turn, can affect the outcomes of dynamical processes operating on those networks. Since exhaustive sampling is not possible, we propose a novel evolutionary framework for mapping this structural diversity. The three main features of this framework are: (a) subgraph-based encoding of networks, (b) exact mutations based on solving systems of Diophantine equations, and (c) heuristic diversity-driven mechanism to drive resolution changes in the MapElite algorithm.We show that our framework can elicit networks with diversity in their higher-order structure and that this diversity affects the behaviour of the complex contagion model. Through a comparison with state of the art clustered network generation methods, we demonstrate that our approach can uncover a comparably diverse range of networks without needing computationally unfeasible mixing times. Further, we suggest that the subgraph-based encoding provides greater confidence in the diversity of higher-order network structure for low numbers of samples and is the basis for explaining our results with complex contagion model. We believe that this framework could be applied to other complex landscapes that cannot be practically mapped via exhaustive sampling