Use of discrete event simulation in hospital capacity planning

In recent years, the healthcare industry is undergoing a rapid expansion in the United States. For healthcare facilities, resource planning at early design stage is a critical step before architectural design. The ‘resources' here refer to both long term resources (pods, rooms, beds, configuration of one pod) in terms of capacity and configuration, and short term resources(staffs, equipments) in terms of capacity and allocation. To achieve performance targets defined by the clients, such as staff/equipment/bed utilization efficiency, average waiting time of all patients, turn away rate, an assessment and verification at the preliminary planning stage is necessary. There are at least two methods to solve this problem. The first is analytical in nature, relying on queuing theory, and falls under the industrial engineering field. The other is computational in nature, relying on process simulation, and specifically discrete event simulation. While queuing theory is easier to conduct, usually requiring less data, and providing more generic rules than simulation, simulation methods result in detailed information about patient flow modeling and deliver more accurate results. This paper is divided into three parts. The first part introduces queuing theory and discrete event simulation in terms of their principles, features and applications in healthcare planning. This is followed by a case study in the ED using discrete event simulation to plan pod configuration and number of pods for an emergency department. During this process, the simulation tool is introduced as an example instrument for advanced DES simulation. The paper ends with a discussion of outcomes. (1) DES is capable to differentiate between alternatives with small changes, and can be widely used to do capacity planning for healthcare facilities. (2) the chosen simulation tool supports the modelling and analysis steps well

A data-centric bottom up model for generation of stochastic internal load profiles based on space-use type

There is currently no established methodology for generation of synthetic stochastic internal load profiles for input into building energy simulation. In this paper, a Functional Data Analysis approach is used to propose a new data-centric bottom-up model of plug loads based on hourly data monitored at a high spatial resolution and by space-use type for a case-study building. The model comprises a set of fundamental Principal Components (PCs) that describe the structure of all data samples in terms of amplitude and phase. Scores (or weightings) for each daily demand profile express the contribution of each PC to the demand. Together the principal components and the scores constitute a structure-based model potentially applicable beyond the building considered. The results show good agreement between samples generated using the model and monitored data for key parameters of interest including the timing of the daily peak demand

Bayesian calibration of building energy models for energy retrofit decision-making under uncertainty

Retrofitting of existing buildings is essential to reach reduction targets in energy consumption and greenhouse gas emission. In the current practice of a retrofit decision process, professionals perform energy audits, and construct dynamic simulation models to benchmark the performance of existing buildings and predict the effect of retrofit interventions. In order to enhance the reliability of simulation models, they typically calibrate simulation models based on monitored energy use data. The calibration techniques used for this purpose are manual and expert-driven. The current practice has major drawbacks: (1) the modeling and calibration methods do not scale to large portfolio of buildings due to their high costs and heavy reliance on expertise, and (2) the resulting deterministic models do not provide insight into underperforming risks associated with each retrofit intervention. This thesis has developed a new retrofit analysis framework that is suitable for large-scale analysis and risk-conscious decision-making. The framework is based on the use of normative models and Bayesian calibration techniques. Normative models are light-weight quasi-steady state energy models that can scale up to large sets of buildings, i.e. to city and regional scale. In addition, they do not require modeling expertise since they follow a set of modeling rules that produce a standard measure for energy performance. The normative models are calibrated under a Bayesian approach such that the resulting calibrated models quantify uncertainties in the energy outcomes of a building. Bayesian calibration models can also incorporate additional uncertainties associated with retrofit interventions to generate probability distributions of retrofit performance. Probabilistic outputs can be straightforwardly translated into a measure that quantifies underperforming risks of retrofit interventions and thus enable decision making relative to the decision-makers' rational objectives and risk attitude. This thesis demonstrates the feasibility of the new framework on retrofit applications by verifying the following two hypotheses: (1) normative models supported by Bayesian calibration have sufficient model fidelity to adequately support retrofit decisions, and (2) they can support risk-conscious decision-making by explicitly quantifying risks associated with retrofit options. The first and second hypotheses are examined through case studies that compare outcomes from the calibrated normative model with those from a similarly calibrated transient simulation model and compare decisions derived by the proposed framework with those derived by standard practices respectively. The new framework will enable cost-effective retrofit analysis at urban scale with explicit management of uncertainties.PhDCommittee Chair: Augenbroe, Godfried; Committee Member: Choudhary, Ruchi; Committee Member: Guillas, Serge; Committee Member: Park, Cheol-Soo; Committee Member: Wu, Jef

Energy performance of a hybrid DSF-inspired solar heating façade for office buildings

Double-skin façade (DSF) is a passive design strategy that enhances building energy performance and improves indoor thermal comfort. In addition, DSF has been proposed as a hybrid façade that uses a cavity to preheat fresh air supplied to an air-handling unit (AHU) to reduce energy consumption for heating. However, to the authors' knowledge, there is no study about the design of DSF tailored for the hybrid system application yet. Therefore, this study focuses on the usability of DSF as a hybrid system and evaluates the performance. First, parametric analysis of the hybrid solar heating façade geometry and thermal properties of glazing and absorber materials was performed to identify the most influencing design parameters. Second, the multivariate linear regression (MLR) model was developed to predict the performance of all parameters comprehensively affecting the hybrid solar heating façade. Finally, the performance of various design alternatives for hybrid solar heating façade that provide the minimum fresh air supply was evaluated through case studies. The analysis results confirmed that the hybrid solar heating façade can reduce the heating energy due to the preheating effect by up to 38%

Design and analysis of a window-integrated passive system (WIPS) with the use of solar heat gains

As one of the passive building design strategies to decrease ventilation energy consumption, several window-integrated passive systems (WIPS) have been developed and implemented into buildings. This study proposed a new WIPS inspired by a double skin façade (DSF) design that provides pre-heating and ventilation by utilizing solar heat gains collected in the air cavities. The performance of the proposed WIPS was analyzed and developed based on different design parameters such as material, width, height, shape, depth, and opening area, for the wintertime in Seoul, Korea. Furthermore, the computational fluid dynamics model, Fluent 2021 R2 with the RNG k-epsilon turbulence model, was used for the simulation. To focus on the buoyancy effect occurring in the air cavities, the influence of wind was excluded from the CFD modelling and analysis. Therefore, 0 Pa pressure differences between the inlet and outlet of the room were applied. The results showed that the buoyancy effect increased in the WIPS with a wider and higher inflow cavity than a case with narrower and shorter cavity, also utilizing glass material for the cavity surfaces exposed to the outside resulted in absorbing more solar radiation and more buoyancy effect. Moreover, the converged shape cavity increased the volume flow rate of the cavity due to the increased air velocity from the Venturi effect. Overall, all design parameters can impact the performance of WIPS either by hindering or assisting the airflow of the WIPS

Exploring the impact of different parameterisations of occupant-related internal loads in building energy simulation

A building energy simulation relies on accurate parameterisation of occupant-related internal loads to simulate a realistic energy balance within a building. The internal loads are inextricably linked to occupant behaviour, both directly through the contribution of occupant heat output to thermal energy balance and indirectly via the interactions between occupants, appliances and building services. While occupancy itself is difficult to measure directly, most buildings possess a wealth of data in the form of monitored electricity consumption in varying degrees of resolution. These data, particularly plug loads, may be used to inform the model of occupant-related internal loads. Different approaches to parameterisation of plug loads have been investigated, with the purpose of exploring the conditions that might lead to preference of one approach over another. The models have been tested through a case study and simulation results have been compared against a range of response variables. Conclusions have been drawn as to the most important features of plug load parameterisation for a model to be used for forecasting future demand.ISSN:0378-7788ISSN:1872-617