The current world population of 7.3 billion is expected to reach 8.5 billion by 2030, 9.7 billion in 2050 and 11.2 billion in 2100, according to a new United Nations Department of Economic and Social Affairs (UN DESA) report, World Population Prospects: The 2015 Revision. This rate of population growth will have an immense impact on environmental sustainability. The increased growth will have a directly proportional effect on demand for housing, energy, food and other resources that are necessary for survival. Increase in urbanisation and buildings will be an inevitable solution to cater to increased housing demand. According to a publication done in 2015 by the National Climate Change Secretariat (NCCS), buildings account for 9 per cent of total carbon dioxide (CO2) emissions in Singapore. It is much higher in the United States, where buildings contribute 39 per cent of CO2. With the rate of building growth predicted, it is crucial that our buildings are sustainable and do minimum damage to the already vulnerable state of our planet.
WHAT IS VIRTUAL MODELLING?
As defined by IGI Global, virtual model is a digital representation of the physical object and serves as a basis for simulating the fabrication process and the structural behaviour of the parts. The virtual model can also be used to prototype the object.
Many of the virtual modelling trainers limit their education to creating models, but do not provide much information on its application. Virtual modelling has a wide range of application areas. Building is one of the areas that are exploiting the field of virtual modelling by creating a digital prototype of an existing or non-existing building and predicting its behaviour in different conditions. Such models can be detailed (contains information of every room in the building) and dynamic (simulates changing parameters like sun path, weather conditions, building schedules, etc.) in nature, depending on the requirements of application. Predictions can be in terms of energy usage, potential of solar energy on rooftops, extreme environment scenarios, etc.
WHAT ARE SMART AND EFFICIENT BUILDINGS?
Smart building is a commonly used term for a building that has automated operations such as heating, air-conditioning, ventilation, lighting and security. The Internet of Things (IoT) plays a major role in enhancing the ‘smartness’ of buildings. It is important to have smart buildings because better control over operations enables efficient use of energy in buildings.
ROLE OF ERI@N IN VIRTUAL MODELLING
The Nanyang Technological University (NTU) is one of the top universities in Asia that have rightly recognised the importance of sustainability and integrated sustainability as a part of their academic and research institutes. Being a university and institute of higher education, it inherently shoulders the responsibility of pioneering innovation and social change. As centres for leading education, research and innovation, universities are the key places to address global issues and foster progressive action among current and future generations.
ERI@N is one of the research institutes at NTU that try to bridge the gap between university research and industries by helping to translate research outcomes into industry and practice.
The EcoCampus Initiative is a novel flagship RD&D programme of ERI@N, built on applied research and test-bedding of innovative technologies. The vision of EcoCampus is to make NTU the Greenest campus in the world and sets an impactful target of reducing energy, water and waste intensity by 35 per cent by 2020 (baseline, 2011). Apart from achieving the ambitious goal, it provides a platform for research, education and demonstration with an emphasis on industry participation. EcoCampus has successfully collaborated with more than 20 industries and transformed the NTU campus into a super test-bed. The test-bed provides an opportunity for industries to co-develop and demonstrate their novel energy-efficient solutions with NTU. The successful solutions are further deployed in the campus at a larger scale and eventually commercialised. This symbiotic relationship helps EcoCampus reach its goal and assists the industries with commercialising their solutions.
With a significant number of test-bed solutions, EcoCampus faces a challenge of determining the solutions that should be deployed at a larger scale in the campus. The challenge does not stop at picking out the technologies. The most optimum scale and location in which the chosen technologies are to be deployed are equally important. The use of energy modelling technique and software becomes indispensable when such decision-making is involved. There are many building energy simulation software available nowadays. To aid such decision-making, EcoCampus has collaborated with IES to exploit their IESVE technology.
IES is recognised as a world leader in three-dimensional (3D) performance analysis software that is used to design tens of thousands of energy-efficient buildings across the globe, and today its capabilities are expanding from use on individual buildings to help in creating sustainable communities and cities. The technology also helps to uncover hidden cost, energy and carbon savings, aiding smarter energy-efficient choices across new building investments, building operation and refurbishment of existing buildings. The technique of modelling is a combination of forward modelling (modelling for building and HVAC system design and associated design optimisation) and data-driven approach that makes use of existing buildings for establishing baselines and calculating retrofit savings.
The IES platform is being used in NTU to develop a 3D virtual model of the campus.
The model helps in identifying the most sustainable technologies that should be deployed throughout the campus. It also helps determine the scale and location of the chosen technologies. In addition to the technology performance analysis, IES’ innovative process solution—Ci2 (Collect, Investigate, Compare, Invest)—is deployed to optimise the building performance in NTU campus. This paper focuses on the requirements of setting up such baseline models and the effort required calibrating them for analysing technologies.
METHODOLOGY
Visualisation and campus model simulation
A quick overview of the entire community is useful early on in the project life cycle. The IES’ Community Information Model (CIM) technology was exploited for visualisation of resource use at campus level. It also provides an inbuilt simulation engine to provide what is commonly referred to as ‘quick-and-dirty’ results. Although these results are casually referred to as ‘dirty’, they can still provide a good overview of expected results. The IES core engine Virtual Environment (VE) was also used for capturing simulation results of the whole NTU campus. The model was simulated for one average year at an hourly interval. The average year data is based on data collected for 15 historic years.
Business as usual – baseline model
It is crucial to develop a good baseline model. The baseline model defines the ongoing status of the building. Any predictions that are made are based on the baseline model. Hence, if the baseline model is inaccurate, predictions will not be reliable as well. It is usual practice to validate baseline models against monthly utility bills. For the NTU baseline campus, an accuracy of 91 per cent for total energy consumption was achieved. This implies that the modelled baseline data is 91 per cent accurate as compared to real
data that was collected over the years. An accuracy of over 90 per cent is encouraging for this scale of modelling since many assumptions need to be used for simulations. An accuracy of 97 per cent was achieved in the case of chillers.
Conclusion on campus simulation
Each technology was simulated and results were compared to identify technologies that can achieve the most energy savings. It is also important to compare savings against consumption breakdown. The savings from individual lighting were more as compared to chillers, but since the chillers consume more energy, the savings can be more effective. All these technologies when implemented together have a potential to save close to 23 gWh of energy every year, which translates to almost SGD5 million.
Detailed building simulation
As a part of this project, along with the whole campus, every room in 21 buildings (20 academic NTU buildings and one JTC-owned Clean Tech One) was simulated in detail. The 3D model of each building in scope was generated at room level. The input data required was:
- Construction: Details of construction materials used in the building, including external walls, internal walls, windows, etc.
- Internal gain: Refers to input of all entities that emit heat and needs air-conditioning to cool down the space. This includes occupancy schedule (details of people occupying room space), lighting schedule, AHU schedule, etc.
- HVAC: Heating requirements of building space are not discussed in this article since tropical countries only require cooling load. Air-conditioning consumes about 50 per cent of the total energy load in a typical commercial building. While simulating buildings in detail, air handling unit (AHU) layouts as well as chiller details was simulated wherever applicable.
CONCLUSION
An accurate baseline design model is necessary to get close to expected energy consumption and associated cost savings. However, a good baseline design model is dependent on the quality of data. While analysing data, the modeller should: check for change in operation pattern in energy meter data; change in temperature and air flow control set point; as well as any suspicious high energy consumption. Any high cooling load spikes should also be taken into consideration. Based on the results, the modeller may need to either remove some outliers or further investigate the reason for outliers, if any. Once achieved, this baseline model that reflects the building’s actual consumption can be used to virtually identify and optimise building performance. While comparing technology, it is also important to compare different parameters—such as energy savings per unit of area and population—for making informed decisions.
PRIYANKA MEHTA
Project Manager at EcoCampus, Energy Research Institute @ NTU (ERI@N)
Priyanka Mehta manages projects dealing with energy modelling and simulation. Most of these projects are done with EcoCampus industry collaborators. Mehta has a background in computer modelling and simulation, data analytics and geographic information systems. Her interests lie in virtual environments and uncovering the stories that may be hidden behind data numbers. She also has sound interest in understanding the nexus between different sectors of an urban environment. Prior to embarking on research, she was mostly involved in working with consultants in projects with Singapore government agencies.
