Recent reports suggest that ‘energy efficient buildings’ are almost never as efficient as their intended design. Findings from the government’s low-carbon building programme, administered by the Carbon Trust, have shown that some buildings consume as much as three times their designed energy efficiency.
This could require some explaining on the part of an FM when faced with an understandably perplexed occupier, who not only has to pay higher then expected energy bills, but who may have paid a premium to locate themselves in such a building in the first place.
In recent years, there has been a decisive move towards developing and retro-fitting commercial buildings to ensure that they are as energy efficient as possible. This trend towards sustainability is motivated, at least in part, by public sentiment, and policed by a growing body of regulation.
For regulations to be meaningful, they need to be underpinned by standard measurements and the relatively new technique of energy modelling performs this important role. Energy models form the basis of such key regulations as the UK Building Regulations Part L Conservation of Fuel and Power (BRUKL), and Energy Performance Certificates (EPCs), part of the Energy Performance of Buildings Directive (EPBD), which are in turn adopted under other benchmarking schemes such as Breeam.
Proposals under the government’s Green Deal, part of the Energy Bill, will place an even greater emphasis on EPCs: from 2018, any commercial property with less then an ‘E’ rating (an ‘A’ rating is given to the most energy efficient buildings and ‘G’ to the least) must have a green deal assessment and implement any works that pass a test known as the golden rule.
Both use the same calculations set out under the National Calculation Methodology (NCM), which was developed by the Building Research Establishment.
Under the NCM, in order to pass building regulations, the building’s carbon emission rate must better the target emission rate. Basic energy models are used to determine the emission rate, such as the Simplified Building Energy Model (SBEM). In this model, a standardised ‘use of building’ is adopted for calculations, together with the designed efficiency of heating, ventilation, air conditioning, hot water systems, lighting systems, as well as their associated controls.
Counting the cost of energy
However, as many FMs know, there is no such thing as a standardised use of a building. In the real world, building services do not operate under test conditions. Occupying an ‘energy efficient building’ that meets certain regulations based on energy modelling, does not necessarily mean FMs will see reduced energy bills. Performance is an output not an input and therefore hard to accurately predict through the use of energy modelling.
Ensuring designed efficiency is met, or bettered, during occupation requires knowledge of the building, its design, its systems, occupancy and detailed knowledge of consumption through metering. It can be extremely time-consuming and require specialist knowledge. It is not enough to know that a building is consuming too much energy one needs to be able to understand why.
Plenty of systems exist to support building managers from Display Energy Certificates (DECs) to sophisticated building management and automated monitoring and targeting systems that help keep track of energy consumption. All these can act as useful diagnostic tools.
Similarly, whole-life systems, which are employed during the design of a building, can be used after construction and commissioning. Building Information Modelling (BIM) is an example of this, but its use after commissioning is limited in terms of day-to-day operations and energy management.
BIM, in-spite of its title, is not strictly speaking a modelling tool, rather a management system with integrated design capabilities. With an interactive library of objects and data, it allows designers and developers to work closely together around design, construction and fit-out. In doing so, the system helps to keep track of building costs in terms of construction and across the whole building lifecycle.
However, the data produced from BIM would form part of a Dynamic Simulation Model (DSM), which accurately models a building, its design, its services and the interaction with the environment.
Under the EPBD, buildings with complex atrium and airflows, or ventilation strategies such as night ventilation, can only be modelled using DSM.
The data taken from BIM would be entered into a DSM, allowing designers to determine a building’s expected operational performance. It may be the case that in order to pass or better BRUKL, new requirements for material, plant or component parts, for example, are re-entered into BIM and so forth.
Model behaviour?
All modelling techniques are weakened by poor data. The model is only as good as the modeller and, as shown above, the design process is fluid. In order for the model to reflect the actual building, the data needs to be accurate and all the main stakeholders, such as architects and engineers, as well as contractors, need to use BIM. This process needs to be strictly maintained throughout construction.
A simple example of BIM being corrupted and affecting the accuracy of a model could be when a designer specifies a certain lamp efficacy, but the contractor subsequently installs a different system with a lesser efficacy, and this change is not entered into BIM. Considering the vast number of component parts to a building and its services and the array of contractors and subcontractors, it’s easy to see how a model’s results could be skewed due to the sheer amount of data that needs to be input.
However, the development of BIM undoubtedly has the potential to change the way the construction industry works, allowing the full spectrum of professionals and contractors to sing from the same hymn sheet. It avoids replication of technical drawings, helps ensure the accuracy of the model, and means there is more scope to iron out potential errors during the design stage, where it is far less expensive to remedy mistakes.
BIM was used with great success during the construction of Heathrow’s Terminal 5 and is said to have reduced build costs by 10 per cent.
Once T5 was built, the BIM was passed over to BAA to form part of its future maintenance programme, allowing managers to access detailed information on, for example, construction type, plant make, model, capacity, refrigerant type and fuel type.
As we move towards a low-carbon economy and the cost of energy increases, actual building performance will become more important than designed efficiency. Understanding this ‘performance gap’ will become crucial, especially in a critical environment where emissions matter, such as in any estate that falls within the Carbon Reduction Commitment Energy Efficiency Scheme.
As designers, engineers and energy managers come up with new ways to help reduce energy consumption and carbon emissions, inevitably the number and the complexity of systems – such as lighting, heating and those governing or monitoring – also increase. This has the potential to make buildings and their controls very complicated and susceptible to human error. Indeed, this is already the case with many existing systems.
Simplifying complexity
But some technologies are being developed specifically to provide simplified, often automated support for building managers.
Complex Systems Engineering (CSE) science is a relatively new field of research that provides a mechanism to handle an increasingly complex world.
The Mitre Organisation in the US is driving this research with the aim of applying systems engineering and advanced technology to areas of national interest, such as defence. Globally, CSE is already used in power stations to reduce carbon emissions with some success.
How an emerging science, designed to handle complexity, can help manage the built environment remains to be determined. But the potential applications could be numerous.
CSE can, and is, used to model any organisation, their building, or buildings. It provides automated decision support, taking into account all of the interactions within the complex system, modelling them in the form of a virtual world.
However, one of the biggest flaws of CSE lies in how complex systems are modelled and simulated.
This is to do with modelling methodologies, of which there are traditionally two types: fundamental and empirical.
The former uses global knowledge that is easily modelled using mathematical equations. An example might be the designed coefficient of performance (CoP) of a heatpump. This knowledge has a short shelf life due to plant characteristics changing over days/seasons and through its lifecycle. Thus, to remain accurate, the model needs to adapt and change in line with the plant.
Empirical modelling uses data taken from actual plant operation to build a relational model of the process. An example would be to look at the actual energy consumption of a heat pump and not the designed CoP.
Such models are easy to establish, but lose the ability to incorporate global knowledge as they are only concerned with data relationships and not the physical relationships that exist within a system.
DSM and BIM use fundamental knowledge in their programming, whereas energy management systems use empirical knowledge. For this reason, the application of DSM and BIM post-construction is so far limited.
However, Australian company Synengco Pty appears to have resolved the problems of both methodologies through the development of a self-learning model that incorporates both local and global knowledge.
The organisation’s technology has been tried and tested around the world and is currently used to manage around $8 billion of assets, primarily fossil fuelled power stations.
Because the model is self-learning, it is able to work in real time, removing the need for the retrospective analysis of data.
Experts believe that major opportunities exist at the transition between design and commission, between commissioning and operation and then in the ongoing operation by identifying performance deviations and eliminating waste or duplication.
Such a system could help identify whether a building’s actual running efficiency differs from its designed efficiency and where the deviations exist.For example, it would be able to alert a building manager to a change in consumption due to a change in area occupancy, or identify in real time a potential problem with plant before it becomes critical.
This has proved invaluable to the power station industry where the ability to identify a deviation from optimum performance in real time can have a significant impact on consumption and CO2 emissions.
The human factor
However, Professor Ian Sommerville of the Scottish Informatics Computer and Science Alliance, an alliance of Scottish Universities that has carried out extensive research into CSE, warns that although these systems may be self-learning, it doesn’t mean that results won’t need checking and auditing by qualified personnel.
Professor Tony Day, director of the Centre for Efficient and Renewable Energy in Buildings (CEREB), points to the variety of already complex building and energy managements systems in the market. “Although these developments are very interesting, if they are too complicated they will simply not be used properly,” he says.
The one common factor around all operational buildings is that they contain people, who are unpredictable. The most efficient heat pumps available will not run efficiently if a window is left open, or diffuser covered up. However, having access to better information in real time might allow FMs to react more quickly to the numerous social, environmental and engineering factors that affect a building’s operational performance.
