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.

On the sensitivity of buildings to climate

Parag Rastogi

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.

EPFL2016

Simulating occupant presence and behaviour in buildings

Jessen Page

Various factors play a part in the energy consumption of a building : its physical properties, the equipment installed for its functioning (the heating, ventilation and air-conditioning system, auxiliary production of electricity or hot water), the outdoor environment and the behaviour of its occupants. While software tool designers have made great progress in the simulation of the first three factors, for the latter they have generally relied on fixed profiles of typical occupant presence and associated implications of their presence. As a result the randomness linked to occupants, i.e. the differences in behaviour between occupants and the variation in time of each behaviour, plays an ever more important role in the discrepancy between the simulated and real performances of buildings. This is most relevant in estimating the peak demand of energy (for heating, cooling, electrical appliances, etc.) which in turn influences the choice of technology and the size of the equipment installed to service the building. To fill this gap we have developed a family of stochastic models able to simulate the presence of occupants and their interactions with the building and the equipment present. A central model of occupant presence, based on an inhomogeneous Markov chain, produces a time series of the number of occupants within a predefined zone of a building. Given a weekly profile of the probability of presence, simplified parameters relating to the periods of long absence and the mobility of the person to be simulated, it has proven itself capable of reproducing that person's patterns of occupancy (times of first arrival, of last departure and periods of intermediate absence and presence) to a good degree of accuracy. Its output is used as an input for models for the simulation of the behaviour of occupants regarding the use of appliances in general, the use of lighting devices, the opening of windows and the production of waste. The appliance model adopts a detailed bottom-up approach, simulating each appliance with a black-box algorithm based on the probability of switching it on and the distribution of the duration and power of its use, whereas the interaction of the occupant with windows is determined by randomly changing environmental stimuli and the related thresholds of comfort randomly selected for each occupant. When integrated within a building simulation tool, these stochastic models will provide realistic profiles of the electricity and water consumed, the wastewater and solid waste produced and the heat emitted or rejected, both directly or indirectly by the occupant.

EPFL2007

Toward Assessing the Sensitivity of Buildings to Changes in Climate

Marilyne Andersen, Sönke Frederik Horn, Parag Rastogi

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.