A comparative study of benchmarking approaches for non-domestic buildings: Part 1 – Top-down approach

Benchmarking plays an important role in improving energy efficiency of non-domestic buildings. A review of energy benchmarks that underpin the UK’s Display Energy Certificate (DEC) scheme have prompted necessities to explore the benefits and limitations of using various methods to derive energy benchmarks. The existing methods were reviewed and grouped into top-down and bottom-up approaches based on the granularity of the data used. In the study, two top-down methods, descriptive statistics and artificial neural networks (ANN), were explored for the purpose of benchmarking energy performances of schools. The results were used to understand the benefits of using these benchmarks for assessing energy efficiency of buildings and the limitations that affect the robustness of the derived benchmarks. Compared to the bottom-up approach, top-down approaches were found to be beneficial in gaining insight into how peers perform. The relative rather than absolute feedback on energy efficiency meant that peer pressure was a motivator for improvement. On the other hand, there were limitations with regard to the extent to which the energy efficiency of a building could be accurately assessed using the top-down benchmarks. Moreover, difficulties in acquiring adequate data were identified as a key limitation to using the top-down approach for benchmarking non-domestic buildings. The study suggested that there are benefits in rolling out of DECs to private sector buildings and that there is a need to explore more complex methods to provide more accurate indication of energy efficiency in non-domestic buildings

Energy use predictions with machine learning during architectural concept design

Studies have shown that the actual energy consumption of buildings once built and in operation is often far greater than the energy consumption predictions made during design—leading to the term “performance gap.” An alternative to traditional, building physics based, prediction methods is an approach based on real-world data, where behavior is learned through observations. Display energy certificates are a source of observed building “behavior” in the United Kingdom, and machine learning, a subset of artificial intelligence, can predict global behavior in complex systems, such as buildings. In view of this, artificial neural networks, a machine learning technique, were trained to predict annual thermal (gas) and electrical energy use of building designs, based on a range of collected design and briefing parameters. As a demonstrative case, the research focused on school design in England. Mean absolute percentage errors of 22.9% and 22.5% for annual thermal and electrical energy use predictions, respectively, were achieved. This is an improvement of 9.1% for the prediction of annual thermal energy use and 24.5% for the prediction of annual electrical energy use when compared to sources evidencing the current performance gap

Improved benchmarking comparability for energy consumption in schools

The method behind the UK Display Energy Certificate (DEC) improves the comparability of benchmarking by accounting for variations in weather and occupancy. To improve the comparability further, the incorporation of other features that are intrinsic to buildings (e.g. built form and building services) deserve exploration. This study investigates the impact of these features and explores ways to improve further comparability in benchmarking the energy performance of schools. Statistical analyses of approximately 7700 schools were performed, followed by analyses of causal factors in 465 schools in greater detail using artificial neural networks (ANNs), each designed to understand and identify the factors that have significant impact on the pattern of energy use of schools. Changes in the pattern of energy use of schools have occurred over the past four years. This fact highlights issues associated with static benchmarks. A significant difference in energy performance between primary and secondary schools meant that it was necessary to re-examine the way non-domestic buildings are classified. Factors were identified as having significant impact on the pattern of energy use. The characteristics raise new possibilities for developing sector-specific methods and improving comparability

Effects of elevated carbon dioxide levels on response speed in cognitive test

To explore the associations of exposure to carbon dioxide with adults' response speed, 69 participants were invited to participate in the experiment conducted in an environmentally controlled chamber. Participants were exposed alone in three separate sessions, each lasting one hour, with a fixed ventilation rate, temperature and relative humidity level and the CO2 levels fixed at 600ppm, 1500ppm and 2100ppm, respectively. A validated neurobehavioral test battery, the Behavioural Assessment and Research System (BARS) was used to assess participants' cognitive performance, and response times were collected. Response speed was assessed in ten different tests. After adjusting for potential confounders (age, gender, and education), results showed no significant differences in eight out of the ten neurobehavioral tests. For the Selective Attention test, participants responded faster (lower response time) under CO2 levels of 2100ppm compared to 600ppm (adj.β-coef. -17.57, 95% CI (-29.45, -5.68), p-value=0.004). For the Progressive Ratio Test, participants' response times significantly decreased with CO2 levels increased. Results indicate no statistical link between CO2 levels and response speed, with only two out of ten comparisons being significant

Low carbon building performance in the construction industry: A multi-method approach of system dynamics and building performance modelling

The construction industry contributes significantly to energy consumption and carbon emissions. Moreover, people spend more time inside buildings, so their health is increasingly influenced by indoor environmental conditions. When considered through these lenses, the concept of total building performance can span energy consumption, the associated CO2 emissions, and indoor environmental quality (IEQ). At the individual project level, building underperformance with respect to energy and IEQ is frequent, and the ex post performance gap is partially attributed to the construction project management and operations phase of the building lifecycle. This underperformance motivates the research of this paper into the construction process outcomes in terms of energy performance and IEQ, and ways to reduce the performance gap. The paper develops a multi-methodology framework to analyse the effect of building development project process on energy performance and IEQ from an operations management perspective. The framework couples system dynamics modelling of construction project management to building performance modelling. The paper details the way they are coupled, the application steps and data requirements, so that they can be applied on a case by case basis. The aim is to combine operations management to building performance disciplines and deliver insights for industry practitioners and policy makers

Project management operations and building performance in the construction industry: A multi method approach of applied in a UK public office building

The “performance gap” in the UK building industry is a persistent problem as new building development projects underperform more often than not. Underperformance has to be addressed as the building sector is responsible for a large share of CO2 emissions in the UK. The “performance gap” arises in part, because building project development involves operations with several stages and actors of different motivations. The outcome of the building project in terms of quality is important and has implications for energy consumption, carbon emissions and occupant well-being. We develop a system dynamics model of building project development operations to explore building quality implications for energy consumption and Indoor Environmental Quality (IEQ). To do this, we couple the system dynamics model to a building physics model and apply them in an empirical case of a recently completed building project. The building performance model is developed and calibrated to reproduce the actual energy performance of the building based on one year of monitoring and post commission data. This is used as a reference point for the system dynamics model that explores additional scenarios of how project operations could deliver better total building performance

Bridging the Gap: the need for a systems thinking approach in understanding and addressing energy and environmental performance in buildings

Innovations in materials, construction techniques and technologies in building construction and refurbishment aim to reduce carbon emissions and produce low-energy buildings. However, in-use performance consistently misses design specifications, particularly those of operational energy use and indoor environmental quality. This performance gap risks reducing design, technology, sustainability, economic, health and well-being benefits. In this paper, we compare settings of the Chinese and the UK buildings sectors and relate their historical context, design, construction and operation issues impacting energy performance, indoor environmental quality, occupant health and well-being. We identify a series of key, common factors of ‘total’ building performance across these two settings: the application of building regulations, the balance between building cost and performance, skills, construction and operation. The dynamic and complex interactions of these factors are currently poorly understood and lead to building performance gaps. We contend that a systems approach in the development of suitable building assessment methods, technologies and tools could enable the formulation and implementation of more effective policies, regulations and practices. The paper illustrates the application of the approach to the UK and Chinese settings. A full application of a systems approach may help to provide a more dynamic understanding of how factor interactions impact the ‘total’ building performance gaps and help address its multiple causes

Reducing emissions in London schools with photovoltaics

This paper examines the potential for PV to improve the performance of primary schools in London. Disaggregate data including energy use is compared with modelled PV generation, showing that electricity demand could theoretically be met in 59% of the schools investigated. The impact of several key factors is then considered, including architectural heritage, building age and form. The results show that the greatest PV potential exists in newer schools, as well as those that are shorter and with less dense forms

Towards the low carbon transition in the construction industry: A multi-method framework of project management operations and total building performance

The building sector is a large contributor to energy consumption and global carbon emissions. In urban environments, most people spend a large amount of their time in buildings, and their indoor environmental conditions can affect occupant health. The total building performance thus spans energy consumption, carbon emissions, and indoor environment. Underperformance in the building sector is frequent, and it is attributed partially to upstream process of construction project management and operations. Current project management approaches focus on quality, cost and time, so a new a framework is required to study this process in terms of total energy performance and explore ways to reduce the total performance gap. A multi-methodology framework is developed in the paper to analyse the effects of building development project process from an operations management perspective, on building energy consumption, carbon emissions, and indoor environmental quality (IEQ). The framework couples a system dynamics project development model to a building physics model. The paper details the steps of the framework along with the data requirements and the way the two models are coupled, so that it can be replicated on a case by case basis