On the sensitivity of buildings to climate:the interaction of weather and building envelopes in determining future building energy consumption

Building simulation requires a large number of uncertain inputs and parameters. These include quantities that may be known with reasonable confidence, like the thermal properties of materials and building dimensions, but also inputs whose correct values cannot be known with absolute certainty, notably weather and occupancy. A simulation run is not, strictly, a prediction. Since the parameters and calculations are approximations of real-world phenomena and materials, the exercise is essentially uncertain. Regardless of whether simulation is interpreted as a prediction or an approximation indicative of average behaviour, including explicit bounds of uncertainty is more informative for a decision-maker than a single point estimate. This thesis presents results for two related but independent proposals for sensitivity and uncertainty analyses in building simulation, particularly to weather. The first is a novel, generalisable procedure for generating synthetic weather data to carry out a Monte Carlo experiment with a building simulation model. The second is a technique for training emulators or response surfaces to rapidly obtain estimates of performance outputs from simulation models, using Gaussian Process regression on small training data sets. The two parts, together and separately, enable the quantification of the lack of knowledge about an input, and the impact of this uncertainty on the final results. The synthetic weather time series developed are an ensemble of realistic hourly data whose mean statistical characteristics are close to the typical year used to generate them. The procedures developed are generalisable with minimal expert input. We avoid presenting a unified model for all climates, leaving some tuning parameters like the extent of correlation, and the unknown coefficients of stationary time series models, to be calculated empirically (based on the typical file of a given climate). The emulators are created using regression, comparing the performance of classical parametric regression with a non-linear technique based on Gaussian random processes. Our proposal trains reliable models on small samples, reducing the computational burden, and gives an explicit estimate of the uncertainty for a prediction, since the response at any sampled point is modelled as a Normally-distributed random process. Once again, we avoid a unified emulator or regression model because the response from one building (defined by its geometry and usage in this case) is not necessarily an appropriate description of the response of another. This work is a step towards practical tools for the use of building simulation in a stochastic paradigm. Both elements of the thesis contribute toward explicitly estimating the uncertainty in the results of building simulation, using empirical or data-driven techniques. The types of the time series and emulator models are general enough to work on any climate or building, with parameters obtained from the simulated/typical sample at hand, but the importance of different aspects and the nature of a buildingâs response are determined uniquely (i.e., parameter values). The work is easily extensible to the analysis of the sensitivity of a building, or groups of buildings, to any inputs. The concepts proposed in this thesis may also be used for stochastic optimisation and models to predict performance metrics other than the annual sum of energy

Embedding Stochasticity in Building Simulation Through Synthetic Weather Files

This paper presents an attempt to create synthetic weather data for stochastic building simulation. The synthetic data are created entirely from the freely available Typical Meteorological Year (TMY) weather files using time series models and resampling. The generated data turn out to be representative of recorded data for our case study without any prior ‘knowledge’ of the long term distributions of meteorological parameters. The current model does not address spells above or below some temperature of interest (e.g. heat waves), and the authors are working to incorporate that in future work. Another avenue for further exploration is modifying the mean to incorporate the results of Regional Climate Models for future conditions. Correlation of the synthetic data with synthetic solar radiation and humidity has been verified and the authors’ work with this ensemble of weather time series of interest will be presented in future publications

Generation of Weather Files Using Resampling Techniques: An Exploratory Study

Simulating a building to predict its performance over the course of a full year requires an accurate representation of the stable and representative weather patterns of a location, i.e. a weather file. While weather file providers give due consideration to the stochastic nature of weather data, simulation is currently deterministic in the sense that using one weather file always generates one performance outcome (for a given set of building parameters). Using a single time series or aggregated number makes further analysis and decision-making simpler, but this overstates the certainty of the result of a simulation. In this paper, we investigate the advantages and disadvantages of incorporating resampling in the overall simulation workflow by comparing commonly used weather files with synthetic files created by resampling the temperature time series from the same weather files. While previous studies have quantified uncertainty in building simulation by looking at the calculation itself, this paper proposes a way of generating multiple synthetic weather files to obtain better estimates of expected performance. As case studies, we examined the performance of the ‘original’ and synthetic files for each of a sample of world climates

Incorporating Climate Change Predictions in the Analysis of Weather-based Uncertainty

This paper proposes randomly-generated synthetic time series incorporating climate change forecasts to quantify the variation in energy simulation due to weather inputs, i.e., Monte Carlo analysis for uncertainty and sensitivity quantification. The method is based on the use of a small sample (e.g., a typical year) and can generate any numbers of years rapidly. Our work builds on previous work that has raised the need for viable complements to the currently-standard typical or reference years for simulation, and which identified the chief components of weather time series. While we make no special efforts to reproduce either extreme or average temperature, the sheer number of draws ensures both are seen with either the same or higher probability as recent recorded data

Robustness Assessment Methodology for the Evaluation of Building Performance with a view to Climate Uncertainties

This paper describes a new methodology to assess the robustness of building performance in the long term with a probabilistic approach. The aim is to include uncertainties related to climate change predictions as well as the intrinsic uncertainties in weather files describing them. A case study focussing on refurbishment strategies of a realistic building in Turin is presented to demonstrate the methodological steps. The main outcome is that it is advisable to have outcomes in terms of ranges of energy consumption instead of single output values to evaluate energy efficient design solutions in both present and future years

Multi-Objective Optimisation for Tuning Building Heating and Cooling Loads Forecasting Models

Machine learning (ML) has been recognised as a powerful method for modelling building energy consumption. The capability of ML to provide a fast and accurate prediction of energy loads makes it an ideal tool for decision-making tasks related to sustainable design and retrofit planning. However, the accuracy of these ML models is much dependant on the selection of the right hyper-parameters for specific building dataset. This paper proposes a method for optimising ML model for forecasting both heating and cooling loads. The technique employs multi-objective optimisation with evolutionary algorithms to search the space of possible parameters. The proposed approach not only tune one model to precisely predict building energy loads but also accelerates the process of model optimisation. The study utilises a simulated building energy data generated in EnergyPlus to demonstrate the efficiency of the proposed method, and compares the outcomes with the regular ML tuning procedure (i.e. grid search). The optimised model provides a reliable tool for building designers and engineers to explore a large space of the available building materials and technologies

Gaussian-Process-Based Emulators for Building Performance Simulation

In this paper we present a novel emulator of a building simulator for the simulation-assisted design of high performance buildings. Our emulator is based on Gaussian-Process (GP) regression models. Such non-linear models are better suited than linear models as emulators because the simulator itself is a collection of non-linear models based on differential equations. We show that our proposed emulator is about 3 times more accurate than linear models in predicting the output of the simulator, achieving an average error of around 10-25 kWh/m2 for prediction of energy outputs that are in the range of 10-800 kWh/m2, compared to an error of around 50-100 kWh/m2 obtained by using linear models. Our emulators also heavily reduce the computational burden for building designers who rely on simulators. For example, the emulator can first be trained with observations from the simulator using a wide variety of building designs and weather data. This pre-trained model can then be used by building designers for exploration of new designs by predicting the performance of new buildings very quickly (in just a few milliseconds). We expect our approach to be particularly useful for Uncertainty Analysis (UA), Sensitivity Analysis (SA), robust design, and optimisation

Toward Assessing the Sensitivity of Buildings to Changes in Climate

Substantial numbers of existing and new buildings are expected to survive long enough to experience perceptible shifts in climate ‘normals’ (averages). To predict a building's response to changes in typical weather, two inputs are required: weather data representing this change, and suitable metrics to compare building performance across different climate normals. This paper presents initial work on a proposed method for assessing the sensitivity of new or existing buildings to climate change. This method begins with a selection of weather files to represent climate change, then quantities a building's passive performance in those climates using an enthalpy-based metric, and ends with a graphical analysis of the performance of the building in different climates to assess its robustness. In this paper, we propose an objective performance metric based on the extent to which a building creates indoor conditions passively, i.e. without auxiliary systems. Initial work suggests that the performance assessment carried out here is reproducible and applicable for indoor environment design and evaluation in different ranges of climate change. This approach enables a comparison of building performance without the bias introduced by inherent differences in climatic conditions

Pulmonary Function Tests In Young Healthy Subjects Of North India

Study Objectives : The diagnosis of disease done by skiagram can be substantiated by pulmonary function tests. Substantial data of Indians on PFTs is not available. The present study therefore has been planned on young healthy north Indians. Setting : 119 males and 49 female medical students of North India. Measurements : PFT's, T.V. FEV1, FVC, FER and PEFR were measured. P<0.05 was considered as significant. Results : In North Indian males, mean T. V was 437.56 ± 65.83 ml, FEV1 3.26 ±041 L, FVC 3.82 ± 0.48 L, FER 85.09 ± 2.42% and PEFR was 495.42 ± 101.82 L / min. In North Indian females, average T. V was 386.12 ± 37.90 ml, FEV1 2.39 ± 0.38 L, FVC 2.79 ± 0.43 L, FER 85.38 ± 257% and PEFR was 307.12 ± 75.74 L / min. Conclusions: Males in comparison to females had more value of PFTs. All the PFTs showed positive correlation with Height, Weight and Surface area except Tidal Volume and FER

The sensitivity of predicted energy use to urban geometrical factors in various climates

Urban morphology, including building typology and layout, has a significant influence on the built environment’s access to the sun, which impacts its energy exchange with the environment. This energy exchange is a strong factor in determining the comfort levels of occupants in buildings and the energy consumed to reach comfort. The influence of urban form has been quantified in previous studies for certain building typologies and programs for specific climates (i.e. location-specific case studies). We are interested in taking this further to assess the variation, due to climate, of the influence of different urban forms on the urban energy balance. This is part of a larger project to study the interaction between form and climate vis-à-vis energy and comfort in buildings. In this paper, we explore this issue through simulation, in various climates, of 3D neighbourhood models. These models consist of a series of parametrically generated variations on building typologies like block, L-shaped, and courtyard block. Each neighbourhood alternative is described through a set of geometrical parameters including the form factor, window-to-floor and plot ratio. We used an extensive database of heating and cooling uses generated by simulating each variant in a representative set of climates to assess the sensitivity of energy use to the geometrical descriptors and climate types. This is done using a regression equation whose input parameters are easily calculable, e.g. form factor, and whose output is an estimate of simulated energy use. The aim of exploring this relationship is to use it to assess the suitability of different urban forms in a given climatic context. Moreover, it provides a promising route to avoid the necessity of detailed energy simulations in comparing the performance of different early urban design alternatives