acute hospital buildings)
The reader will now have an insight into the principals of building engineering physics as it impacts the practice of engineering design of buildings and particularly the impact of key design decisions related to In-use on energy consumption and associated carbon emissions. The second research question remains concerning the robustness of the theory of In-use and the science that is the foundation of it: Is the theory sufficient and is it adequately supported by science? Only through investigation of the theory and the science can this question be answered. This is the objective of this next section.
Understanding the theory of In-use in relation to energy consumption requires understanding the causes of energy consumption from In-use. The author has discussed earlier in this thesis some of those causes (please refer to p77) and in this section the author will discuss the science behind these causes.
It became clear in the literature review that understanding the theory of In-use requires the use of simulation tools. This need arises because in attempting to understand the causative factors that impact energy consumption performance in buildings, researchers have been obliged to model complex inputs to the analysis. This is where simulation can be of great benefit and is thus an important tool in the analysis of building performance (Augenbroe, 2011). In doing so this potentially enables the user to gain wider insights into the significant complexities ‘real world’ problems, probably more so than other forms of computational analysis and mono disciplinary tools (Hensen and Lamberts, 2011). For these reasons, the literature review commences with an investigation into the principles of simulation, particularly as it relates to the study of the theory of In-use and the science that supports it. It is important also because there still remains the question as to how the science can be most effectively leveraged in pursuit of optimised building performance.
3.3.1 - Overview – the role of simulation
The literature review identifies that a substantial body of knowledge is emerging focused on working with a significant number of variables that impact the energy consumption of buildings In-Use. A consistent feature of the wider range of studies considered for the literature review concerns the impact of the building occupant on energy consumption. In other words research is focused upon the
causative impacts of occupancy on consumption - in particular to understand the relationship between the metabolic nature of occupancy and engineering system dynamics. The author will demonstrate evidence of the need to understand the factors that cause occupancy presence in space and time as much as the quantum of occupancy The practice of simulation to understand the impacts of occupancy on energy consumption is a common feature of research in this field. Simulation enables complex systems involving multi-dimensional/ disciplinary interrelationships to be understood. In the engineering design process simulation will often be used as a decision support tool as illustrated in Figure 22 the following page.
Figure 22 - A benefit of simulation: to enable deep understanding of the impact of input variables, or assumptions, on outcomes
The literature review identifies the use of simulation in:
1. Whole building simulation during the design phase. (P. Hoes et al., 2009), (Short et al., 2010).
2. Whole building simulation during the In-use phase. (Short et al., 2009), (Claridge, 2011)
3. Specific areas of a facility requiring detailed analysis. (Beggs et al., 2008), (Khan et al., 2012)
4. In-use operational analysis28 (Jun et al., 1999), (T. McNulty and Ferlie, 2002) (Hall, 2006), (M.M. Gunal and Pidd, 2010)
The practice of simulation enables the users to reduce the number of variables29 in analysis, and consequently reduce uncertainty in the outcome of the design and engineering process. This presupposes that the building will have been constructed, commissioned and calibrated according to the assumptions of the engineering designers. Short et al (Ibid) report on post-occupancy evaluation of a naturally ventilated office building with complex controls making substantial use of natural ventilation. Despite sophisticated modelling, including Computational Fluid Dynamics (CFD) the building failed to respond as designed. Indeed the building suffered from failures identified in the PROBE studies some 10 years earlier, most notably in the inability of contractor and subs-contractors to design and install according to the specification.
In the examples identified above, they can be divided into two categories of study: a) Predictive and b) Causative. Predictive analysis is often applied in the engineering design process to predict how the building environment would respond to a given set of parameters. The simulation facilitates a decision making process, in terms of establishing specific target values that should result in predictable performance In-use. In research it will be demonstrated how predictive analysis enables researchers to understand the sensitivity of values that lead to certain results. However, if the results were not to be predictable, then causative analysis would be required, to establish the reasons for the poor predictability of the results. Referring back to the study by Short et al (Ibid) had the designers learned the lessons from the PROBE studies, they may have predicted the impact of failures in critical parts of the system. Whilst this has little relevance to the pure science of building engineering physics, it does point to the need to consider building engineering not just from the application of the science but also in terms of how other engineering systems can
28
The literature review identifies a long history of simulation in healthcare service design. Evidence goes back to the mid 1960’s where simulation was carried out using Fortran.
29
impact the environmental balance of the building. For example, the authors make reference to the simulation model failing to consider the impact of revolving doors on the air flows and uncontrolled infiltration within the building, and they explained how this lead to a failure in performance. Did this arise because the science was unclear, or because of the inadequate application of the science? The science is very clear as to the impact of revolving doors on the energy balance in a building (C. Younes et al., 2011). The modelling method is also examined in Underwood and Yik (Op Cit, pp122-126). It would appear that it was the poor application of the physics that led to the failure.
The analysis of the reasons for failure was seeking out the causative factors that led to those failures. In-use studies such as those carried out by Short et al, would provide essential criteria to identify these ‘what-if scenarios’. This suggests that in studying the building engineering physics and the application of it in simulation studies, failure analysis needs to consider the impact of assumptions both during the design phase as much as during the construction and commissioning phases.
Within certain bounds the greater the complexity of the model then the less the potential for error contained in it. This is not to suggest that models cannot be too complex, because that is certainly a danger for simulation. Nevertheless, the opposite is also true that the simpler the model, possibly the more assumptions that are made and the fewer the number of variables that are considered the less potentially reliable the simulation. Hensen and Lamberts (Op Cit) make the point that:
“The aim should be to keep the model as simple as possible to meet the objectives of the simulation study.”
So to reduce the aforementioned uncertainty it typically requires a corresponding increase in data complexity and input parameters reflecting the ‘real- world’, but in doing so the simulation team must recognise the level or resolution required to enable an appropriate level of understanding to be achieved from the simulation, such as causative or predictive. But what if the data does not exist such that it could provide the required values for the input variables and parameters? Even if it does, can it be modelled such that it can be processed? If it does not, then the investigators would need to develop analytical models that substitute the complexity of the data or inherent uncertainty in it.
This is where theoretical constructs can be deployed, such as Markov Chains: a stochastic method in which future states of a system are dependent on its current state. The implementation of such models requires a clear understanding of current performance so that the stochastic nature of the system (in this case user behaviour) can be transformed into a set of transition states, thus simplifying the inherent complexity of a large data model. This implies extensive survey in order to understand current performance (causative analysis). However it also assumes that low order states are subsumed into higher order states which studies have proven does not necessarily reflect the ‘real world’ (Gillespie, 1992). Logically it could be reasoned that because such analysis is in effect ‘bottom-up’ and the model attempts to encompass all behavioural states (higher and lower order), then such models could become very large and complex. In the context of the research question being explored in this thesis the use of Markov Chains might be used to predict users behaviour (predictive analysis) in terms of the probability that they would consume certain resources when in specified states or in transition states. The need for bespoke analysis to provide appropriate value for a simulation was discussed in an overview of the subject (V. Fabi et al., 2011), who concluded that:
“Moreover, software packages are not nowadays capable of adequate evaluation of scenarios explaining the influence of occupant behaviour, but this is a crucial point in the efforts to minimize energy consumption.”
The inference here is that whilst it is certainly possible to predict through simulation as to where/ when users will be in a particular space and so potentially cause energy to be consumed, the question would remain as to the probability that they would cause consumption of one type or another. This would be the role of a bespoke analysis and is what has been defined as ‘activity recognition’ (Duong et al., 2006)
Other types of analysis using simulation technology can be used: a) Agent- Based Simulation (ABS), alternatively known as Agent-Based Modelling (ABM), or b) Discreet Event Simulation (DES) in predictive analysis, or c) System Dynamics (SD). In his review of 15 years of application of ABM, Squazzoni (Squazzoni, 2010) defines the purpose of it as a computational method to create, analyse and experiment with model composed of agents that interact with the environment. Whereas DES is defined
as another computational method focused managing discrete events in systems. Applied to healthcare it is defined as an operational research technique that allows researchers to assess the efficiency of healthcare delivery systems and to ask ‘what-if’ questions (Jun et al., 1999). Simulation is a technique to predict reality or at least to predict certain insights on reality. Where the DES is designed to measure state or elucidate rules at a system level of abstraction, ABS is the means by which the measured state or rules can be understood at an entity level of abstraction.
DES is ideal where the context for the research question involves complex organisational process involving for example occupants as part of a networked system need to be investigated. The research question is likely to be process-orientated, and thus the need is to understand the system operation (top-down, or a ‘Birds eye’ view as it has often been referred to). This would be useful where the system as whole needs to be modelled rather than the entities within it. For the context of this thesis, it is here where the presence of users in the hospital could be identified, because they will be modelled as part of the system, and not as entities with individual behaviours within the system. It is where the probability of events is sampled at each discrete event in the simulation. (Siebers et al., 2010) suggest that this is an ideal application of DES.
SD is primarily used to understand how a system works. It has been described as deterministic and not stochastic where SD models a system as flows, akin to a fluid. SD is usually applied at a strategic level of abstraction, unlike DES, which is applied at a tactical level within a system (S.C. Brailsford and Hilton, 2001). For this reason it has often been used to inform policy decision making.
Siebers et al (Op Cit) argue that the application of ABS is ideal where the research question is focused, for example, on developing a ‘bottom-up’ understanding of the impact of a behavioural model where the individual roles of agents within a system is pre-determined. ABM applies this understanding to study the impact of agents on other agents acting within that system. In the context of this thesis, the use of ABM could be required where the research questions are directed to understanding how an individual user type (agent) of groups of user types (agents) could interact with the engineering systems. More generally ABM has been defined in these terms, where Siebers et al (Op Cit) state:
conducting experiments with this model for the purpose of understanding the behaviour of the system and/or evaluating various strategies for the operation of the system. In ABMs, a complex system is represented by a collection of agents that are programmed to follow some (often very simple) behaviour rules.”
“…To follow some (often very simple) behavioural rules” is the key issue here. How could it be possible to define all the human behavioural rules that might be reasonably evidenced in a systems or interacting systems? If they have to be ‘simple’, what scientific value can such models provide, beyond the ‘simple’? How could such a simulation be calibrated? The challenge has been described in these terms by Squazzoni (Op Cit), in reflecting on 15 years of attempts to use ABM successfully in social sciences:
“A first critical point is the lack of a common methodological standard on how to build, describe, analyse, evaluate and replicate an Agent Based Model.”
Squazzoni quotes Gintes (2007) who observed that:
“This lack has seriously penalised the wider recognition of ABM in standard science.”
In the healthcare environment such as the author will be studying, there will be a larger number of variables that need to be considered in the design of the engineering systems, and these will interact directly or indirectly on the simulation. As such these could impact behaviour rules, and make the operation described by Siebers et al very complex. For example, referring to the author’s diagram in Figure 22, a key variable, which could also be an assumption in a simulation of the impacts of the user on the engineering system of the building, would be the extent to which the user intervened in the control of the building automation system. This example was considered by Zimmermann (2010). Zimmermann proposes that the agent specification needs to consider user roles, user process and behaviours, all of which Squazzoni and Gintes (Op Cit) dispute are capable of being accurately modelled.
The use of simulation in healthcare has a long history. Jun at al (Op Cit) is the most widely referenced. It is important to study this area of literature because it is from the perspective of clinicians rather than from the perspective of engineering designers.
In this regard a more recent survey of literature (Fone et al., 2003) comprising in excess of 2000 abstract and 900 full articles found extensive use of DES, and Markov Chain analysis, but there was no reference to ABM. Of particular importance to this thesis was the conclusion of the authors of the paucity of outcomes measured through evaluated implementation. This suggests that the work of McNulty and Ferlie is of particular importance as a point of reference to this thesis because it does at least provide an evidence base for analysis of implemented change. From the analysis of practice in Section 3.1 it suggests two principal research objectives in the theoretical analysis of the In-Use characteristics of the building. Referring back to Figure 17 and the discussion on p73:
1. Seeking to understand the impact of occupant behaviour and building performance. The impact will be concerned with opening windows, and doors, and the management of controllers for heating and cooling systems and the activation of auxiliary systems such as lighting, small power and equipment. 2. Seeking to understand occupant presence and distribution and in particular to
understand the potential impact of occupant presence on the design of building engineering systems.
Robinson and Haldi (2011) characterise the relationship between these two fields of occupant analysis in the following terms:
“…in general the predictive accuracy of a model of occupants’ behaviour is contingent on the accuracy of the model predicting their presence; likewise, the estimation of associated metabolic heat gains. This is so in all of the above cases. It is thus of primordial importance that these models be theoretically sound and rigorously validated. But this task is complicated by the difficulty in reliably detecting the number of occupants within a building zone throughout the period of interest or, better yet, tracking occupants’ movement throughout a building whilst present.”
Even reason would dictate that predicting occupant presence in buildings must be a first order priority over understanding how users may or may not interact with it. Clearly the interaction can only take place when an occupant or occupants is/ are present. A point emphasised by Page et al. (2008). Yet this is not to discount the importance of behaviour, only that in order to effectively develop reliable models of
such behaviour, the matter of occupant presence must also be understood if holistic understanding of the impacts of occupancy is to be reliably forecast. This is what CIBSE Guide A (Op Cit) encompasses when setting out the requirements for calculation of internal heat gains. Mahdavi and Proglhof (2009) emphasises the importance of both perspectives:
“Accordingly, many recent and ongoing research efforts attempt to construct models for passive and active occupancy effects on building performance… Specifically, long-term high-resolution empirical data on people's presence and control-oriented actions in buildings can support the generation of general patterns of user control behavior.”
It is from this reasoning that there is a focus in research on understanding the combined impacts of both perspectives. (Bourgeois, 2005), (Page et al., 2007), (Virote and Neves-Silva, 2012).
Figure 23 - The relationship between building occupant and energy / resource consumption (redrawn from Page et al, 2007)
Page (Op Cit) illustrates the interactions between occupant and energy impacts in Figure 23. The diagram clearly shows the primordial position of occupancy presence
in relation to occupancy behaviour in the use of the systems described in the author’s diagram in Figure 17 (p74). The European Environment Agency report: Achieving Energy Efficiency Through Behaviour Change, (A. Barbu et al., 2013)states:
“A growing body of evidence in academic literature demonstrates that there is potential for energy savings due to