Ruchi Choudhary

Architectural layout design optimization

This article presents an optimization model of the quantifiable aspects of architectural floorplan layout design, and a companion article presents a method for integrating mathematical optimization and subjective decision making during conceptual design. The model presented here offers a new approach to floorplan layout optimization that takes advantage of the efficiency of gradient-based algorithms, where appropriate, and uses evolutionary algorithms to make discrete decisions and do global search. Automated optimization results are comparable to other methods in this research area, and the new formulation makes it possible to integrate the power of human decision-making into the process.

A hierarchical Bayesian framework for calibrating micro-level models with macro-level data

Owners of housing stocks require reliable and flexible tools to assess the impact of retrofits technologies. Bottom-up engineering-based housing stock models can help to serve such a function. These models require calibrating, using micro-level energy measurements at the building level, to improve model accuracy; however, the only publicly available data for the UK housing stock is at the macro-level, at the district, urban, or national scale. This paper outlines a method for using macro-level data to calibrate micro-level models. A hierarchical framework is proposed, utilizing a combination of regression analysis and Bayesian inference. The result is a Bayesian regression method that generates estimates of the average energy use for different dwelling types whilst quantifying uncertainty in both the empirical data and the generated energy estimates. Finally, the Bayesian regression method is validated and the use of the hierarchical Bayesian calibration framework is demonstrated.

Quantitative risk management for energy retrofit projects

This article presents a risk analysis method based on Bayesian calibration of building energy models. The Bayesian approach enables probabilistic outputs from the energy model, which are used to quantify risks associated with investing in energy conservation measures in existing buildings. This article demonstrates the applicability of the proposed methodology to support energy saving contracts in the context of the energy service company industry. A case study illustrates the importance of quantifying relative risks by comparing the probabilistic outputs derived from the Bayesian approach with standard practices endorsed by International Performance Measurement and Verification Protocol and ASHRAE guideline 14.

DELORES – an open-source tool for stochastic prediction of occupant services demand

This paper provides a detailed technical description of DEmand LOad REconStructor (DELORES), a new, open-source modelling tool for stochastic simulation of occupant services demand in buildings. By services, one means usually: illumination, sustenance (by way of food preparation), communication (by way of information technology services), and thermal comfort. In the building simulation environment, however, these services are commonly represented through annual transient profiles of internal heat gains, electricity loads, and set point temperatures. The intended capability of DELORES is to stochastically generate such profiles for use in common building energy models, such as TRNSYS and EnergyPlus, and do so whilst presenting users with a straight-forward, easy-to-use interface. By furthering the dissemination of this tool in the public domain, and encouraging it is future development, it is hoped that multidisciplinary research related to stochastic occupant behaviour and energy demand will become an easier task.

Influence of district features on energy consumption in non-domestic buildings

The spatial variability of gas consumption is investigated in non-domestic buildings across districts of Greater London, UK. It is argued that the energy consumption of a building is to some extent influenced by where the building is located in a city, due to contextual features of its own district as well as those of neighbouring districts. Using Bayesian spatial models, the analysis suggests the energy consumption due to the influence of district features can be quantified and dissociated from the energy consumption associated with the physical features and operational characteristics of the buildings. An important distinction is made between extrinsic values of energy consumption (district features) and intrinsic values (building characteristics, management and operation). The results indicate that 90% of the mean value of extrinsic gas consumption across districts in London is between –42 and 87 kWh/m2. Buildings located in districts that have positive values of extrinsic gas consumption consume more gas over and above their expected intrinsic value of gas consumption. The novel features of this study are in the quantification and propagation of district-scale features to their influence on buildings, and the reduction in uncertainties around the mean value of gas consumed by different building types.

Bayesian inference for thermal response test parameter estimation and uncertainty assessment

This work was supported by the Japan Society for the Promotion of Science (JSPS) (KAKENHI, grant numbers 26709041 and P16074).

Economic, Climate Change, and Air Quality Analysis of Distributed Energy Resource Systems☆

Abstract This paper presents an optimisation model and cost-benefit analysis framework for the quantification of the economic, climate change, and air quality impacts of the installation of a distributed energy resource system in the area surrounding Paddington train station in London, England. A mixed integer linear programming model, called the Distributed Energy Network Optimisation (DENO) model, is employed to design the optimal energy system for the district. DENO is then integrated into a cost-benefit analysis framework that determines the resulting monetised climate change and air quality impacts of the optimal energy systems for different technology scenarios in order to determine their overall economic and environmental impacts.

Multi-Dimensional Simulation of Underground Subway Spaces Coupled with Geoenergy Systems

Old and deep subway lines suffer from overheating problems, particularly during summer, which is detrimental for passenger comfort and health. Geothermal systems could serve as one of the potential energy efficient cooling solutions, compared to energy intensive conventional cooling. The waste heat of the subway tunnel can be harnessed, to provide heating to residential and commercial blocks above the tunnels. This paper presents a multi-scale co-simulation framework for quantifying the amount of useful heat that can be extracted from overheated underground subway tunnels using geothermal heat exchangers. The co-simulation is applied and tested on a representative section of the London Undergrounds Central Line. The Central Line is modelled using a 1D heat and mass transfer model. The geothermal system, on the other hand, is represented using a 3D finite element model. The 1D and 3D models are co-simulated, using the subway tunnels outer wall temperatures as boundary conditions. The model is run parametrically to identify the best arrangement and depth of geothermal heat exchangers for extracting excess heat from subway tunnels. Results show that the depth of 15 m. below the tunnel is sufficient for vertical closed loop heat exchangers to yield temperature drop of 4C in the subway tunnel and platforms. Partially insulated boreholes, alternating between extracting and injecting heat into the soil, are also assessed for their potential to provide heating and cooling demand simultaneously and improve the overall geothermal system efficiency. The heat extracted along a representative section of the tunnels is compared to the heating demand of the buildings above ground.

Influence of error terms in Bayesian calibration of energy system models

Calibration represents a crucial step in the modelling process to obtain accurate simulation results and quantify uncertainties. We scrutinize the statistical Kennedy & O’Hagan framework, which quantifies different sources of uncertainty in the calibration process, including both model inputs and errors in the model. In specific, we evaluate the influence of error terms on the posterior predictions of calibrated model inputs. We do so by using a simulation model of a heat pump in cooling mode. While posterior values of many parameters concur with the expectations, some parameters appear not to be inferable. This is particularly true for parameters associated with model discrepancy, for which prior knowledge is typically scarce. We reveal the importance of assessing the identifiability of parameters by exploring the dependency of posteriors on the assigned prior knowledge. Analyses with random datasets show that results are overall consistent, which confirms the applicability and reliability of the framework.

A simplified method to estimate the free path length variance

Kutruff derived an analytical solution for the prediction of reverberation time in spaces with arbitrary shape. It is the most popular analytical equation that contains shape factors. However, in this equation the free path length variance remains as unknown parameter. Kutruff suggested the implementation of Monte‐Carlo simulation to determine this value for each unique shape. In practice this method is not significantly simpler than a full ray tracing simulation. Fortunately, using methods from the probability field, the free path length variance does have an analytical solution, but this takes such a complicated form that it is not convenient to use. This article treats the development of a simplified method to estimate the free path length variance without losing accuracy when applied to concert halls. A simplified regression model is developed for rectangular shapes based on the analytical solution. This simple model explains 99.8% variance for any rectangular shape up to 1:10:10. Secondly, for arbitr...