Sandhya Patidar

Statistical techniques to emulate dynamic building simulations for overheating analyses in future probabilistic climates

As projections of climate change become more detailed and sophisticated, analysing the effects of these projections on, for example, building performance will become more complex. This study, as part of the Low Carbon Futures project, proposes a method for integrating the latest UK Climate Projections 2009, which are probabilistic in nature, into dynamic building simulation calculations. This methodology offers the possibility that, in an analysis of overheating in buildings, it will be viable for a building designer to assess future thermal comfort of a building in a probabilistic way, with various climate scenarios informing a risk analysis of whether that building will become unsuitable as a working/living environment. To reduce the computational requirements of such an analysis, a series of statistical manipulations and approximations are proposed that serve to reduce substantially the amount of computation that would otherwise be necessary when using such climate projections. The resulting tool, which in essence captures the behaviour of complex simulation models using linear filtering techniques and regression, is successfully validated against results obtained from building simulation software results for a domestic building case-study, including versions of the building with specific adaptation scenarios applied that might offset the predicted overheating.

Tailoring a future overheating risk tool for existing building design practice in domestic and non-domestic sectors

The Low Carbon Futures Project, as part of the Adaptation and Resilience to Climate Change (ARCC) programme, has developed an overheating tool, based on probabilistic UK Climate Projections (UKCP09), to provide design advice for building adaptations in future. For dwellings this tool, initiated by a single simulation, relies on just hourly climate information to predict the internal temperature profile and for a non-domestic building, it includes internal activity profiles to account for lighting, equipment, metabolic gains and air change. To produce a tailored design tool, a qualitative investigation has been carried out to understand current building practices. This investigation shows that the two sectors take a significantly different approach to design, where dynamic building simulation is rare for domestic developments. The diversity of the non-domestic building stock poses different challenges and requires more detail to perform any overheating analysis, with dynamic building simulation playing a key role. The suitability of this tool, and the need to balance complexity and detail with usability and applicability, will be explored for the two sectors, with an approach for implementing this in the future proposed. Practical application: This paper compares current overheating analyses, as reported through correspondence with practitioners, with a suggested approach for a more detailed future overheating assessment using the latest climate projections. The required steps to bridge the gap between current and possible future design methods are explored for both the domestic and non-domestic sectors, with a prototype tool proposed that has been formulated with the aid of industry feedback. The described project is therefore able to translate complex building and climate science into an approach that is potentially useful for building practitioners.

Communicating future overheating risks to building design practitioners: Using the Low Carbon Futures tool

The Low Carbon Futures tool provides a probabilistic assessment of future overheating risks and cooling demands for domestic and nondomestic buildings in the UK. The approach adopted for the development of the Low Carbon Futures tool includes academic rigour within the development of the calculation engine, and also practitioner feedback throughout the process. This paper discusses the journey of the tool from modelling and simulation to the practitioner engagement, which took place by means of a questionnaire, focus groups and interviews with building design professionals aimed at understanding how the issue of overheating in buildings is being addressed. Throughout these events, the synergies between designing for low-carbon targets and designing for a future climate were explored. A final dissemination event was held to identify output styles that could be generated by the Low Carbon Futures tool that would be more practical and useful for specific client types. The workshop discussions serve to shape the outputs from the tool, and the feedback gathered will be used to inform a number of output styles, based on client type. Practical application : This paper outlines the development of the Low Carbon Futures tool for analysing overheating risks in buildings and discusses the practitioner feedback obtained from industry professionals on the use and applicability of the tool, in a final event hosted by the Low Carbon Futures research team in London. This event confirmed that practitioners need to be comfortable with the layout and format of the output in order to communicate its meaning and possible implications to a range of clients. A balanced output is required, which conveys some of the complexity of the underlying analysis, but which is easily understood and conveyed to a potentially lay audience.

Controlling noise-induced behavior of excitable networks

The paper demonstrates the possibility to control the collective behavior of a large network of excitable stochastic units, in which oscillations are induced merely by external random input. Each network element is represented by the FitzHugh–Nagumo system under the influence of noise, and the elements are coupled through the mean field. As known previously, the collective behavior of units in such a network can range from synchronous to non-synchronous spiking with a variety of states in between. We apply the Pyragas delayed feedback to the mean field of the network and demonstrate that this technique is capable of suppressing or weakening the collective synchrony, or of inducing the synchrony where it was absent. On the plane of control parameters we indicate the areas where suppression of synchrony is achieved. To explain the numerical observations on a qualitative level, we use the semi-analytic approach based on the cumulant expansion of the distribution density within Gaussian approximation. We perform bifurcation analysis of the obtained cumulant equations with delay and demonstrate that the regions of stability of its steady state have qualitatively the same structure as the regions of synchrony suppression of the original stochastic equations. We also demonstrate the delay-induced multistability in the stochastic network. These results are relevant to the control of unwanted behavior in neural networks.

Stochastic modelling techniques for generating synthetic energy demand profiles

This paper investigates three stochastic modelling procedures for generating N (user specified) synthetic annual electricity demand profiles at one-minute resolution. The paper reviews previous work in the application of HMM for synthesizing highly stochastic time-series of domestic electricity demand through a sophisticated framework coalescing 480 distinct HMM. The efficiency of a proposed approach for integrating a time-series deseasonalizing technique with a single HMM has been studied in parallel with a compatible stochastic modeling framework of a time-series deseasonalized ARIMA model. Various statistical measures/characteristics of the real and synthetic profiles have been compared for all the three stochastic modelling procedures to identify the most efficient and practically suitable medium for generating synthetic electricity time-series at a fine temporal resolution. Results have been shown for both the individual buildings and the composite (aggregated) profiles of many buildings.

Towards an overheating risk tool for building design

Purpose – The work set out to design and develop an overheating risk tool using the UKCP09 climate projections that is compatible with building performance simulation software. The aim of the tool is to exploit the Weather Generator and give a reasonably accurate assessment of a buildings performance in future climates, without adding significant time, cost or complexity to the design teams work.Methodology/approach – Because simulating every possible future climate is impracticable, the approach adopted was to use principal component analysis to give a statistically rigorous simplification of the climate projections. The perceptions and requirements of potential users were assessed through surveys, interviews and focus groups.Findings – It is possible to convert a single dynamic simulation output into many hundreds of simulation results at hourly resolution for equally probable climates, giving a population of outcomes for the performance of a specific building in a future climate, thus helping the use...

Analysis of probabilistic climate projections: heat wave, overheating and adaptation

Climate change could substantially impact on the performance of buildings in providing thermal comfort to occupants. The recently launched UK climate projections (UKCP09) suggest that all areas of the UK will become warmer in the future with the possibility of more frequent and severe extreme events, such as heat waves. This study, as part of the low carbon futures (LCF) project, explores the consequent risk of overheating and the vulnerability of a building to extreme events. A simple statistical model proposed by the LCF project elsewhere has been employed to emulate the outputs of the dynamic building simulator (ESP-r), which if directly used with the numerous replicated climates available from a probabilistic climate database could be practically challenging. For complex probabilistic climate datasets, we demonstrate the efficiency of the statistical tool in performing a systematic analysis of various aspects of heat waves including: frequency of extreme heat events in changing climate; its impact on overheating issues and effects of specific adaptation techniques applied to offset predicted overheating. We consider a domestic building as a virtual case study. Results are presented relative to a baseline climate (1961–1990) for three future timelines (2030s, 2050s and 2080s) and three emission scenarios (Low, Medium and High).

Delayed feedback control in stochastic excitable networks

A simplified model of a stochastic neural network is considered, being a system of a large number of identical excitable FitzHugh-Nagumo oscillators coupled via the mean field. The possibility to control the global dynamics of this network is investigated. The control tool being probed is Pyrgas delayed feedback constructed and applied through the mean field. It is shown that one can to destroy or diminish stochastic synchronization in a partially synchronized network by a weak delayed feedback under the appropriate choice of delay.