Building In Melbourne Research Articles

Energy, economic, and carbon emission analysis of a residential building with an energy pile system

This paper investigated the combined energy, economic and carbon performances of energy piles used for heating and cooling buildings, an issue that has received limited interest in the past. To address this knowledge gap, a 30-year numerical simulation, calibrated using field data, was conducted for variable building thermal loads, pile spacing, pile length and number of energy piles for a six-storey residential building in Melbourne, Australia. The energy pile system in the annual heating-only mode demonstrated a 75 % reduction in energy consumption and a 5 % reduction in energy costs compared to a natural gas boiler. The energy piles performances in the annual heating-and-cooling mode were significantly higher than in the heating-only mode, demonstrating a 39 % reduction in energy consumption, carbon emissions and energy costs compared to an air-source heat pump. Increasing the pile spacing from 2.05 m to 6.4 m, pile length from 10 m to 20 m, and number of energy piles from 38 to 114 increased the energy, economic and carbon benefits of energy piles by 76 %, 77 % and 119 %, respectively. The results highlight the importance of building thermal loads and pile configuration on assessing the energy, economic and carbon performances of energy piles during early design stages.

Simulation-Based Study on the Effect of Green Roofs on Summer Energy Performance in Melbourne

Green roofs are increasingly recognised as a crucial urban solution, addressing climate change, enhancing energy efficiency, and promoting sustainable architecture in densely populated areas. In this manuscript, the research study delves into the influence of green roofs on energy consumption, focusing on the Treasury Place building in Melbourne, Australia. The utilisation of DesignBuilder and EnergyPlus simulations was explored. Various green roof parameters such as the Leaf Area Index (LAI), plant height, soil moisture, and tree coverage were optimised and compared against base case scenarios. The key findings indicate an optimal LAI of 1.08 for maximum energy savings, with diminishing returns beyond an LAI of 2.5. The soil moisture content was most effective, around 50%, while a plant height of approximately 0.33 m optimised energy reduction. The introduction of 50% canopy tree coverage provided temperature regulation, but increased soil moisture due to trees and their influence on wind flow had an adverse energy impact. These results emphasise the necessity for precise green roof representation and parameter optimisation for maximum energy efficiency. This research offers essential insights for those in urban planning and building design, endorsing green roofs as a pivotal solution for sustainable urban environments.

Energy Consumption Forecasting in Commercial Buildings during the COVID-19 Pandemic: A Multivariate Multilayered Long-Short Term Memory Time-Series Model with Knowledge Injection

The COVID-19 pandemic and the subsequent implementation of lockdown measures have significantly impacted global electricity consumption, necessitating accurate energy consumption forecasts for optimal energy generation and distribution during a pandemic. In this paper, we propose a new forecasting model called the multivariate multilayered long short-term memory (LSTM) with COVID-19 case injection (mv−M−LSTM−CI) for improved energy forecast during the next occurrence of a similar pandemic. We utilized data from commercial buildings in Melbourne, Australia, during the COVID-19 pandemic to predict energy consumption and evaluate the model’s performance against commonly used methods such as LSTM, bidirectional LSTM, linear regression, support vector machine, and multilayered LSTM (M-LSTM). The proposed forecasting model was analyzed using the following metrics: mean percent absolute error (MPAE), normalized root mean square error (NRMSE), and R2 score values. The model mv−M−LSTM−CI demonstrated superior performance, achieving the lowest mean percentage absolute error values of 0.061, 0.093, and 0.158 for DatasetS1, DatasetS2, and DatasetS3, respectively. Our results highlight the improved precision and accuracy of the model, providing valuable information for energy management and decision making during the challenges posed by the occurrence of a pandemic like COVID-19 in the future.

Energy Forecasting in Buildings Using Deep Neural Networks

Abstract The building sector is responsible for the largest portion of the total energy consumption in the United States. Conventional physics-based building energy models (BEMs) consider all of the building characteristics in order to accurately simulate their energy usage, requiring an extensive, complex, and costly process, particularly for existing buildings. In recent years, data-driven models have emerged as an additional path toward the prediction of energy consumption in buildings. The purpose of this work is to present a methodology for predicting the energy consumption of buildings using deep neural networks (NNs). Three machine learning algorithms, including a linear regression model, a multilayer perceptron NN, and a convolutional NN (CNN) model, are proposed to solve an energy consumption regression problem using outside dry-bulb temperature as the only input. To assess these methods, a building in Melbourne, FL, is considered and modeled in EnergyPlus. Ten years of data were used as inputs to the EnergyPlus model, and the energy consumption was calculated accordingly. The input to the machine learning algorithm (average daily dry-bulb temperature) and the output (daily total energy consumption) are used for training. Cross-validation was performed on the trained model using actual weather data measured onsite at the building location. The results showed that all three proposed machine learning algorithms were trained successfully and were able to solve the regression problem with high accuracy. However, the CNN model provided the best results when compared with the other two methods. This work also investigates different data filtering techniques that provide the best positive correlation between inputs and outputs for a similar type of problem. Results from this work aim to be used toward accurate energy forecasting that facilitates achieving higher energy efficiency in the building sector. The presented framework provides a readily simple model that allows accurate prediction of outputs when supplied with new inputs and can be used by a wide range of end users.

Predicting Commercial Building Energy Consumption Using a Multivariate Multilayered Long-Short Term Memory Time-Series Model

The global demand for energy has been steadily increasing due to population growth, urbanization, and industrialization. Numerous researchers worldwide are striving to create precise forecasting models for predicting energy consumption to manage supply and demand effectively. In this research, a time-series forecasting model based on multivariate multilayered long short-term memory (LSTM) is proposed for forecasting energy consumption and tested using data obtained from commercial buildings in Melbourne, Australia: the Advanced Technologies Center, Advanced Manufacturing and Design Center, and Knox Innovation, Opportunity, and Sustainability Center buildings. This research specifically identifies the best forecasting method for subtropical conditions and evaluates its performance by comparing it with the most commonly used methods at present, including LSTM, bidirectional LSTM, and linear regression. The proposed multivariate, multilayered LSTM model was assessed by comparing mean average error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) values with and without labeled time. Results indicate that the proposed model exhibits optimal performance with improved precision and accuracy. Specifically, the proposed LSTM model achieved a decrease in MAE of 30%, RMSE of 25%, and MAPE of 20% compared with the LSTM method. Moreover, it outperformed the bidirectional LSTM method with a reduction in MAE of 10%, RMSE of 20%, and MAPE of 18%. Furthermore, the proposed model surpassed linear regression with a decrease in MAE by 2%, RMSE by 7%, and MAPE by 10%.These findings highlight the significant performance increase achieved by the proposed multivariate multilayered LSTM model in energy consumption forecasting.

On being entirely ordinary

On being entirely ordinary

Analyzing energy consumption patterns of an educational building through data mining

Green building standards were adopted around the world to minimize energy consumption and carbon emission from the building sector. However, designing buildings following the standards does not guarantee a low-energy building. Actual energy consumptions in buildings were reported, on average, 2.5 times higher than predictions during the design stage, which is known as the performance gap. Most building operation managers do not have the tool and knowledge to understand the reasons for such differences. Moreover, current building management systems (BMS) do not have the intelligence to identify such differences and their sources. Understanding the root causes of the performance gap is even more difficult in an educational building because of its different usage patterns and various space types. This research aims to analyze the energy consumption patterns of different spaces in a mixed-use educational buildings and identify possible sources of energy waste using an unsupervised data mining approach. A 5-star Green Star-rated educational building in Melbourne, Australia, was considered for the case study. The results showed electrical and gas energy performance gaps of 2.4 and 3.1 times, respectively, in the studied building. Further analysis revealed that energy consumption during non-working hours was 48% of total energy consumption during the one-year studied period, which is very high and was one of the possible sources of waste. During the holidays, the mechanical system and plug loads ran as per the weekday operating schedule in an empty building, resulting in energy waste. Actual hourly lighting and plug load consumption profiles differed significantly from the predicted profile during design. Based on the findings, several recommendations were made to minimize the performance gap in an educational building.

Saving Mr. Water: anthropomorphizing water promotes water conservation

Anthropomorphism refers to the assignment of human-like attributes to otherwise non-human entities. In the pro-environmentalism literature, there is indication that anthropomorphizing nature can promote green behaviors. The current research examines how anthropomorphizing water can promote water conservation behaviors via environmental locus of control—one primary impediment to people acting in an environmentally-friendly fashion. A randomized controlled field trial was carried out across three apartment buildings in Melbourne, Australia, from July 2018 to June 2019. At the end of the first six months, which served as our baseline, half of the households had received monthly water conservation tips within an e-mail newsletter format with water anthropomorphized; the other half received the same tips without anthropomorphism. In the latter six months, households in the experimental group consumed fewer liters of water than those in the control group. Our end-of-year survey revealed that anthropomorphizing water increased responding households’ internal environmental of control, which then encouraged water conservation behaviors. A follow-up laboratory experiment replicated the results within a controlled atmosphere. Both theoretical contributions and practical implications are discussed.

Enhancement in Free Cooling Potential through Evaporative Cooling Integrated with PCM Based Storage System: Experimental Design and Response Surface Approach

PCM-based thermal energy storage in conjunction with an electric air source is offered as a potential tool to support domestic energy demand reduction while at the same time minimizing supply challenges for the electricity utilities. This study is to advance the use of hybrid ventilation concepts in building design by assessment of PCM storage unit with an operation of the evaporative ventilation system. RSM CCD methods was employed to evaluate and optimize the individual/interactive effect of parameters on the cooling efficiency of the proposed system. Optimization results indicate that maximum efficiency can be achieved when inlet air temperature and velocity of 28 oC and 1.2 m/s when PCM coils number was set to 28, respectively. This research compared the effectiveness of the passive and free cooling application of PCM when applied to a duplex brick veneer building in Melbourne. It was observed that during the studied period, the free cooling application method is more effective than the passive application method in reducing the peak zone temperature.

Fuzzy-stochastic model to assess defects in low-rise residential buildings

Understanding the severity level of defects in buildings can help project managers to take appropriate corrective actions. The objective of this study is to assess the severity of defects in low-rise residential buildings and develop a stochastic model for predicting the likelihood of occurrence of various categories of defects. The defects data for ground floor slabs and walls were extracted from engineering inspection reports of 252 residential buildings in Melbourne, Australia. The analyses were then carried out using Fuzzy Synthetic Evaluation (FSE) and Monte Carlo Simulation (MS) techniques. The outputs of the FSE showed that the severity index of the floor cracks was found to be 3.05 with the equivalent linguistic term of ‘slight’ or 1–2.0 mm in size. Similarly, the severity index of wall cracks was found to be 3.01 with the equivalent linguistic term of ‘slight’ or 1–5.0 mm in size. This indicates that the severity level of defects in floor and wall is low. Likewise, the findings of the simulation analysis revealed that the probability of occurrence of floor cracks with width ranging from 2 to 4 mm is 40.9%, and the chance of occurrence of wall cracks having widths ranging from 5 to 15 mm is 73.6%. The proposed models can assist the regulatory bodies and construction supervisors to assess the probability of occurrence and severity of the building defects in the ground floor slabs and walls of low-rise residential buildings. Further, this study contributes to the body of knowledge by proposing methods to assess defects in buildings.

Analysis of the causes of defects in ground floor systems of residential buildings

The objective of this research is to analyse the interactions among the causes of defects in ground floor systems of low-rise residential buildings, and on that basis, to identify the most significant causes. The data used in this study was extracted from engineering inspection reports of 250 residential buildings in Melbourne, Australia. Association rule mining technique was used to investigate the relationships among the causes of the defects in the ground floor slab systems and to generate support, confidence and lift values. social network analysis was then carried out using the confidence values and the most significant causes of the ground floor slab defect were identified. Inadequate site drainage and soil drainage, with the out-degrees centrality of 4.84 and 4.56, respectively, were found to be the two main causes of distress in ground floor slabs of the residential buildings. On the other hand, landscaping related problem was found to have the lowest out-degree centrality value (1.60) which implies that the occurrence of poor landscaping on certain residential premises might not be the major cause for defects in the slab. The output of this research can provide useful information regarding the causes of cracks in ground floor slab systems of residential buildings. Understanding the causes of the defects, in turn, can help developers and contractors to take appropriate corrective actions or to develop strategies to reduce the occurrence of defects in newly constructed houses.

Design for Dematerialisation: examining an approach for reducing the life cycle energy requirements of residential buildings

Responsible for significant energy demand and emissions, the building sector has an opportunity to contribute to global sustainability efforts. In creating resilient and sustainable human settlements, alternative design approaches that address life cycle energy requirements are needed. Design for Dematerialisation is one such approach that aims to lower life cycle energy requirements by reducing material and resource inputs. Yet, despite its conceptual simplicity, the application of the approach within the building sector and its effect on life cycle energy demand is poorly understood. This study seeks to briefly outline the approach from the perspective of the building sector and examine the effect of dematerialisation actions on the life cycle energy requirements of a multi-residential building in Melbourne, Australia. This study demonstrates that while an effect is observed – most notably in recurrent embodied energy – a partial rebound effect exists. Savings from the reduction of key materials and components are partially negated by the energy intensity of alternative materials introduced, and/or additional elements required to maintain the building’s fitness-for-purpose. The assessment highlights the need for designers to address energy performance holistically, by taking into account the effect of actions on multiple life cycle stages concurrently. By employing this considered approach, dematerialisation has the potential to be an accessible strategy for designers tasked with creating sustainable, resilient urban settlements.

Investigating Daylight in the Apartment Buildings in Melbourne, Australia

Daylighting improves users’ experience in visual comfort, aesthetics, behaviour and perception of space and plays an important part in enhancing the health and wellbeing of occupants inside a dwelling. However, daylighting design is challenging for high rise living since configuration of multiple apartments together often results in deep plans and wrongly oriented apartments with poor daylighting. Melbourne considered as the most liveable city in the world has witnessed a boom in high rise apartments in recent years, where bedrooms were designed without windows or with one small opening. Previous studies indicated that one out of two apartments in Melbourne’s central business district (CBD) failed to provide daylighting in the bedrooms. This has led to amendments in planning policy with the aim of providing access to daylight in all habitable rooms. This paper investigates the daylighting conditions in apartment buildings using field measurement and daylight simulations. Daylight levels in 12 apartment units in Melbourne CBD were measured. Additionally, daylight simulations were conducted to identify ways for optimizing light levels in standard layouts. The field measurements showed that daylighting levels were insufficient in one third the apartments due to the presence of deep floor plates and external obstructions. The results from the daylight simulations showed that window to floor area ratio (WFR) of approximately 30% is required for achieving acceptable daylighting levels in bedrooms that have south orientation.

An approach for sustainable, cost-effective and optimised material design for the prefabricated non-structural components of residential buildings

Offsite prefabrication of the non-structural components of residential and commercial low- and high-rise buildings is becoming increasingly popular due to its sustainability, fast on-site execution, safety and cost-effectiveness. The benefits of offsite prefabrication can be further enhanced if it is coupled with the principles of Design for Manufacturing and Assembly (DfMA), which is based on materials, labours and cost optimisation. Although DfMA has been widely used in automotive and other industries, it is not well-established in the offsite prefabricated construction industry. Moreover, there are no guidelines exist for applying DfMA in prefabricated construction. In this paper, DfMA principles are applied to the most common non-structural members i.e., timber frame wall and the plumbing drainage system of a local residential building in Melbourne, Australia. Site and factory observations for the manufacturing of these components were carried out to collect actual assembly times. Then, using the collected data, the design efficiencies of onsite-, offsite- and DfMA-based prefabrication were computed by developing a user-friendly tool through advanced MATLAB programming. Moreover, from a cost-benefit analysis, DfMA-based prefabrication was found to be better than conventional factory prefabrication of selected non-structural components. Furthermore, based on the results, a pragmatic framework for DfMA based design and execution in a real prefabricated construction project is proposed. The findings of this paper are significant as they can contribute to developing guidelines for DfMA based design of prefabricated non-structural components of residential buildings.

Smoking in apartment buildings - Spatiality, meanings and understandings.

Increased higher density urban living may exacerbate exposure to environmental tobacco smoke. Using a social practices lens, this research explored the locations, experiences and governance of smoking in apartment buildings in Melbourne, Australia, through semi-structured interviews in high-rise buildings ranging from subsidised housing to luxury apartments. Tacit rules on acceptable locations, building rules and smoke alarms in public areas consigned smoking to balconies and building entrances. The perceived health threat from second hand smoke and mental stress due to invasion of olfactory and visual privacy undermined the full enjoyment of the home environment. Interventions may benefit from targeting the link between smoking and balconies.

Review of techniques for identifying the chemical composition of Aluminium Composite Cladding cores

The Grenfell disaster of June 14, 2017 brought into sharp focus the dangers posed by combustible façade materials. Polyethylene core panelling has been implicated in numerous fires such as The Address Downtown, the Saif Belhasa in Dubai and the Lacrosse building in Melbourne. Due to the large number of buildings with potentially combustible cladding, there is significant pressure to find an accurate and fast method to identify such cladding and any associated insulation as part of the overall assessment process. Over the past year, several different approaches to identification have been proposed and in this paper, we recount our expe-riences in developing a methodology to achieve this and discuss the limitations of this method and of competing methods.

Reflections of a green university building: from design to occupation

Purpose The purpose of this paper is to present outcomes from a post-occupancy evaluation (POE) of a sustainable university building development. Design/methodology/approach A POE was conducted for a sustainable university building in Melbourne, Australia. The method included interviews with key stakeholders involved in the design, construction and occupation of the building. The interviews were complemented by conducting a Builder User Satisfaction survey and analysis of two year’s worth of building performance data. Findings While technically the building saw a significant improvement in performance in comparison to existing buildings at the university, it ultimately did not meet its design performance goals as determined by the design rating. The interviews revealed limited formal documenting of lessons learnt and the challenges associated with using a sustainable and innovative building to drive cultural change. A major success was the realisation by the university of the benefits that the systematic POE provided. Lessons are now being applied to other new and refurbished buildings on campus, with POE now an integrated part of these processes. Originality/value While there are some studies of sustainable university operations and buildings, many focus on one or two parts of the process and fail to include evaluation of the full sustainability approach to check if stated goals have been met. This paper begins to address this gap. Learnings from the research are applicable to the wider building development industry and demonstrate the important role universities can play in shaping the sustainability of urban environments.

Energy saving performance assessment and lessons learned from the operation of an active phase change materials system in a multi-storey building in Melbourne

While the energy saving performance of an active phase change materials (PCMs) system in buildings has been widely investigated using prototype-scale experiments and numerical assessments, their performance during the operational phase of a real building has been less understood. This study assessed the energy-saving performance of an active PCM system installed in an eleven storey building in Melbourne. Macro-encapsulated PCM with the phase transition temperature of 15 °C was installed in a large PCM tank. Water was used as the heat transfer fluid (HTF) to extract and store cooling energy from the PCM tank. The performance of the active PCM system was monitored for 25 consecutive months, and the results were analyzed on a seasonal basis. Building design documents and the maintenance manuals were studied to understand the difference between design intent and actual operation. The analyzed results revealed that the active PCM system reduced cooling load on the chiller by 12–37% only during colder months, but, remained dormant during the summer. Even in the case of maximum effectiveness, the PCM tank only utilized 15% of its available heat storage capacity to reduce the cooling load. The factors that contributed to the underperformance of active PCM system include mismatch between designed and actual operation of the PCM system, inefficient operation logic of the system, poor material quality, and limited knowledge of maintenance staffs during the operation stage. The lessons learned from the operation of this active PCM system in this multi-storey building were reported and discussed.

Evaluating the passive and free cooling application methods of phase change materials in residential buildings: A comparative study

The integration of Phase Change Materials (PCMs) in buildings as a potential method to improve indoor thermal comfort can be achieved via three different approaches: passive, active and free cooling. Previous studies on the thermal performance enhancement using these methods revealed that all three methods can improve the energy efficiency or indoor thermal comfort of a building significantly. However, there is no study available in the literature comparing the effectiveness of different PCM application methods. Such comparative analysis is important to understand which application method would be best for increasing the energy efficiency and thermal comfort of a particular building type. The aim of the present study is to compare and analyze the effectiveness of passive and free cooling application methods of PCM in a residential building in Melbourne, Australia. The passive application method utilizes a macro-encapsulated PCM, so-called BioPCM mats, installed in the ceilings of the building. In free cooling, outdoor air was supplied to the indoor after passing it through a PCM containing heat exchanger. The comparative study was carried out using validated numerical models for both application methods. The simulation models were developed using building simulation software EnergyPlus V8.3 and computational fluid dynamics (CFD) software ANSYS V15.1. The results showed that, for the studied building, free cooling application of PCM is more effective than the passive application in reducing the indoor zone temperature. During the studied period of seven days, passive application of 25°C PCM resulted in up to 0.44°C reduction in peak indoor zone temperature compared to 2.63°C reduction in the free cooling application which is about six times of the reduction in passive case. Despite the use of several supporting strategies to improve the performance of passive PCM application, its effectiveness in reducing the peak zone temperature was always found to be lower than the free cooling method. Parametric studies showed that the optimum PCM temperature should be carefully chosen based on the application method as free cooling and passive cooling methods are influenced by outdoor air temperature and indoor zone temperature respectively.

A Comparative Study on the Effectiveness of Passive and Free Cooling Application Methods of Phase Change Materials for Energy Efficient Retrofitting in Residential Buildings

This study compared the effectiveness of passive and free cooling application methods of Phase change materials (PCMs) when used as energy efficient retrofitting in a residential building. A modern duplex residential building in Melbourne, Australia was considered for the case study. In passive application, PCM was installed in the ceilings of the house. In free cooling application, outdoor air was supplied to the indoor after passing it through a PCM storage unit. The study was carried out using building simulation software EnergyPlus V8.4 and computational fluid dynamics (CFD) software ANSYS V15.1. The developed simulation models of passive and free cooling applications were validated using relevant experimental data. The validated simulation models were then used to investigate the effectiveness of these PCM application methods in the selected building. The results showed that, for the studied house, free cooling application of PCM is more effective than the passive application in reducing the internal zone temperature. Under typical summer climatic conditions of Melbourne, free cooling application resulted in up to 1.8°C reduction in zone air temperature, compared to only 0.5°C when PCM was applied as passive heat storage system. The outcome of this study would be helpful in selecting the effective PCM application method for these types of residential buildings in similar climates.