Search

Voltage rise caused by high levels of distributed generation is manifesting as voltage regulation challenges for many electricity network service providers. In this environment it would be ideal to reduce supply voltage magnitudes, however, many network operators are hesitant to do so due to concerns related to consumer appliance performance at reduced supply voltage magnitudes. Voltage regulation requirements are defined by network standards and network service providers must ensure voltages remain within specified limits. Through an evaluation of domestic appliance performance when supplied at various voltage magnitudes, this paper examines the impact of varying voltage levels on residential appliances. Equipment energy demand, operation and actuation were monitored for each applied voltage magnitude. While no equipment failures were recorded, appliance behaviour varied significantly with applied voltage magnitude. Individual appliance conservation voltage reduction (CVR) factors have also been established. The results highlight the importance of good voltage regulation and provide substantiated appliance performance figures for future studies. The outcomes of this paper allow electricity network service providers to understand the implications of supply voltage magnitude on domestic appliance performance, whether it be understating of the impact of higher voltage magnitudes caused by distributed generation or implications of reducing voltage magnitudes to provide headroom for distributed generation integration. [ABSTRACT FROM AUTHOR]

This paper examines how Australian residential load profiles may evolve in the short to medium term future. These profiles can be used to support simulation studies of the future Australian network within an environment that is transitioning to renewable energy and broader use of electricity as a tool for decarbonisation. The daily profiles rely heavily on the Australian Energy Market Operator (AEMO) forecasts for future annual energy usage. The period from 2024 to 2050 will be transformational. In the residential networks, two secular trends are particularly important in expanding residential generation and electrification. New daily load profiles have been constructed using historical Australian profiles and adding additional components for solar generation, battery operation and electrification activities. The entire aggregated residential network is expected to have reverse midday power flow on any average day from 2024 onwards due to the rapid increase in electric vehicle (EV) usage. The domestic energy demand forecasting methodology presented in this work related to Australia can easily be adopted to carry out similar forecasting for any country of the world. [ABSTRACT FROM AUTHOR]

This paper presents a systematic analysis of energy savings and cost benefits associated with several options for integrating energy efficiency and renewable energy technologies. The primary goal of this study is to assess the cost-effectiveness of achieving optimal net-zero energy (NZE) designs for residential buildings in Australia. Specifically, the analysis combines a series of sensitivity analyses and multi-objective optimizations to account for a wide range of design strategies for detached homes in four cities representing different Australian climates. The results indicate that not only are NZE designs technically feasible for all the considered Australian cities, but they are also highly cost-effective. This cost-effectiveness is attributed to the lower installation costs of rooftop PV systems as well as the beneficial interactive effects of proven energy efficiency strategies. Indeed, it is found that the deployment costs of rooftop PV systems can be recovered in less than 4 years. Moreover, the addition of thermal insulation in walls and ceilings can reduce both HVAC capacities and annual energy end-use by up to 59%. Based on an optimization-based design, NZE homes in Australia can have lower construction costs and, ultimately, lower life cycle costs than dwellings built to meet current energy efficiency standards based primarily on stringent building envelope thermal performance. [ABSTRACT FROM AUTHOR]

Energy used in buildings is mainly attributed to provide the desired thermal comfort, which could result in an increase in carbon emission and, in turn, lead to further environmental degradation. A Building-Integrated Photovoltaic Double-Skin Façade (BIPV-DSF) is a promising way to maintain indoor thermal comfort, obtained with low environmental impact and energy consumption. The appropriate design of BIPV-DSFs can maximise indoor thermal comfort and energy efficiency for buildings. This paper presents optimal BIPV-DSF design solutions, which are dedicated to offering comfortable and energy-efficient buildings, through optimisation of the most important design parameters of a BIPV-DSF under three different climate conditions in Australia. The results illustrate how thermal transmittance (U-value) and solar heat gain coefficient (SHGC) of windows of the BIPV-DSF, as the most important design parameters, were optimised for application in the context of different climates, operation modes, and orientations. The paper contributes to the matters concerning the integrated effect of BIPV-DSFs on thermal comfort and energy performance in buildings. [ABSTRACT FROM AUTHOR]

Domestic water heating accounts for 15% to 27% of the total energy consumption in buildings in Australia. Over the past two decades, the latent heat thermal energy storage (LHTES) system has been widely investigated as a way to reduce fossil fuel consumption and increase the share of renewable energy in solar water heating. However, the research has concentrated on the geometric optimisation of the LHTES heat exchanger for the past few years, and this might not be sufficient for commercialisation. Moreover, recent review papers mainly discussed the development of a particular heat-transfer improvement technique. This paper presents perspectives on various solar hot water systems using LHTES to shift focus to on-demand performance studies, as well as structure optimisation studies for faster commercialisation. Future challenges are also discussed. Since the topic is an active area of research, this paper focuses on references that showcase the overall performance of LHTES-assisted solar hot water systems and cannot include all published work in the discussion. This perspective paper provides directional insights to researchers for developing an energy-efficient solar hot water system using LHTES. [ABSTRACT FROM AUTHOR]

Accurate short-term power load forecasting is crucial to maintaining a balance between energy supply and demand, thus minimizing operational costs. However, the intrinsic uncertainty and non-linearity of load data substantially impact the accuracy of forecasting results. To mitigate the influence of these uncertainties and non-linearity in electric load data on the forecasting results, we propose a hybrid network that integrates variational mode decomposition with a temporal convolutional network (TCN) and a bidirectional gated recurrent unit (BiGRU). This integrated approach aims to enhance the accuracy of short-term power load forecasting. The method was validated on load datasets from Singapore and Australia. The MAPE of this paper's model on the two datasets reached 0.42% and 1.79%, far less than other models, and the R2 reached 98.27% and 97.98, higher than other models. The experimental results show that the proposed network exhibits a better performance compared to other methods, and could improve the accuracy of short-term electricity load forecasting. [ABSTRACT FROM AUTHOR]

This paper addresses the optimal pre-cooling problem for air conditioners (AC) used in Internet of Things (IoT)-enabled smart homes while ensuring that user-defined thermal comfort can be achieved. The proposed strategy utilises renewable energy generation periods and moves some of the air conditioning loads to these periods to reduce the electricity demand. In particular, we propose a multi-stage approach which maximises the utilisation of renewable energy at the first stage to satisfy air conditioning loads, and then schedules residual energy consumption of these loads to low price periods at the second stage. The proposed approach is investigated for the temperature and renewable generation data of NSW, Australia, over the period 2012–2013. It is shown that the approach developed can significantly reduce the energy consumption and cost associated with AC operation for nearly all days in summer when cooling is required. Specifically, the proposed approach was found to achieve a 24% cost saving in comparison to the no pre-cooling case for the highest average temperature day in January, 2013. The analysis also demonstrated that the proposed scheme performed better when the thermal insulation levels in the smart home are higher. However, the optimal pre-cooling scheme can still achieve reduced energy costs under lower thermal insulation conditions compared to the no pre-cooling case. [ABSTRACT FROM AUTHOR]

The current process of issuing planning permits mostly relies on checking Land-use Regulations (LuRs) against two-dimensional (2D) analogue or digital proposed development plans. Checking three-dimensional (3D) LuRs within 2D proposed development plans results in challenges for decision-makers to understand LuRs' limits and the impacts of the proposed developments on existing buildings in their surrounded proximity. Given the advancement of 3D geospatial technologies, to overcome such challenges and facilitate the process of issuing planning permits, 3D digital approaches should be developed for effective 3D storage, analysis, and visualisation of 3D LuRs and detection of their potential conflicts. This paper, as part of an internship project with Bentley systems, aims to design and develop a web-based 3D visualisation prototype called iTwin4PP for issuing planning permits using Bentley iTwin platform. This prototype first demonstrates how 3D LuRs related to planning approval can be modelled automatically in 3D and combined with an integrated BIM-GIS environment including BIM designs of the proposed developments and GIS models of planning/city-data. Then, the prototype considers the possibility of 3D spatial analyses (especially proximity analysis) for verifying 3D LuRs automatically to detect potential spatio-semantic conflicts that may arise between modelled LuRs and physical/planning objects. Five LuRs subject to planning approval in Victorian jurisdiction, in Australia, including height limits, energy efficiency protection, overshadowing open space, noise impacts, and overlooking are highlighted. While these LuRs are specific to Melbourne's planning scheme ordinance, we believe that the prototype and encountered challenges in integrating different sources of information especially BIM and GIS, modelling 3D LuRs, and detecting their potential conflicts are common and can be applied in other jurisdictions. [ABSTRACT FROM AUTHOR]

The integration of a CSP tower system with a PV solar field, sharing a thermal energy storage, is modeled and discussed. The tower system uses a new-design solid particle fluidized bed receiver integrated with a thermal storage, where hot particles are directly collected to store daily energy for overnight production of electricity. The PV solar field is aimed to supply the daily energy demand; when there is a surplus of PV energy production, the electric energy is converted to heat and accumulated in the thermal storage too. The integration of the two energy systems is modeled, building efficiency functions for all the sub-components of the integrated plant (heliostat field, receiver, storage, power block, PV field). Yearly simulations are performed for two different locations, Spain and Australia, obtaining that a system with a peak power of 10 MWe CSP + 15 MWe PV can supply—with a limited curtailment—a fraction of more than 60% (respectively, 62% and 68%) of a realistic electric load with a peak demand around 10 MW, to be compared with the 45/47% of the same load obtained adopting a PV-only system with the same overall peak power. In the integrated system, PV directly supplies 40/41% of the load, the remaining 23/28% being produced by the power block (mainly fed by the CSP). [ABSTRACT FROM AUTHOR]

Low-income housing plays an important, but frequently overlooked, role in energy use reduction. Barriers persist for low-income households to participate in energy efficiency programs and adopt efficient lifestyles. However, there has been only limited research into energy efficiency barriers faced by low-income households. Existing energy research studies primarily focus on homeowners whose demographic and socio-economic profiles are likely to be very different from low-income households or renters—limiting the applicability of previous findings to low-income households. This study aims to identify and evaluate the importance of the energy efficiency barriers faced by low-income households. A questionnaire survey was conducted with 212 low-income households in Australia. After randomly dividing the data into calibration and validation samples, an exploratory factor analysis (EFA) of the calibration sample identifies four energy efficiency barrier factors of financial, decision-making, information, and split incentives. These four factors are then validated by confirmatory factor analysis (CFA) of the validation sample in terms of goodness-of-fit, reliability, and validity to confirm financial as the most highly rated energy efficiency barrier. This research contributes to bridging the knowledge gap of the energy efficiency barriers of low-income households and providing a validated CFA model as a tool for assessment. The results provide a better understanding of the barriers involved and research evidence to facilitate the formulation of policies to overcome them. [ABSTRACT FROM AUTHOR]

The relationship between housing quality and energy disadvantage over the long run is a complex interaction of growing interest. This work navigates through conceptual definitions and datasets to highlight gaps in monitoring and evaluating energy disadvantage, particularly the role of housing in perpetuating or mitigating energy disadvantage. We find that in Australia there is no agreed‐upon definition or metrics for energy poverty or energy hardship, nor a panel or longitudinal housing stock energy efficiency dataset and discuss options for remediating this. [ABSTRACT FROM AUTHOR]

Energy poverty is a temporary condition, yet a non‐negligible share of the Australian population suffers persistent energy disadvantage. Using the Household, Income and Labour Dynamics in Australia (HILDA) survey, we observe that single individuals, single‐parent households and those with a disabled household member are at high risk of persistent energy poverty. This is also true for non‐working individuals and immigrants from non‐English‐speaking countries. Nevertheless, highly educated individuals, those living in metropolitan areas and homeowners are less likely to persistently experience energy poverty. Government investment in energy efficiency is crucial to reduce electricity bills and have healthier homes. [ABSTRACT FROM AUTHOR]

Healthcare buildings often have high energy use intensity, which is potentially influenced by a few factors, such as occupancy and climate. A suite of data analysis methods, including principal component analysis and regressions, is applied to analyse monthly electricity data of a modern major hospital in subtropical Australia. The analysis shows that occupancy is not highly correlated with the hospital's electricity use, nor is it important for building energy modelling. However, outdoor environment temperature is highly correlated with the hospital's electricity use. Then, the hospital's electricity uses in 2030 to 2090 scenarios are forecast with future climate files. The impacts are analysed in terms of bill increases and renewable capacity needed to offset the increased electricity use. This study has established a process to predict future hospital energy use using data-driven energy modelling. This succinct article provides vital evidence to support the healthcare sector to continuously improve energy efficiency for health buildings, which is a major asset to adapt to the changing climate. [ABSTRACT FROM AUTHOR]

Moisture-related issues in buildings can accelerate the decay of building materials and increase occurrence of mould, resulting in significant economic and health implications. Transient hygrothermal risks assessment proved to be the most reliable approach, yet its application at early design stages is impeded by the high number of assumptions, input and skills required to be robustly applied. Climate-based indicators can assist in determining effective prevention design strategies based on the magnitude of the potential hygrothermal risks. However, in Australia, the National Construction Code relies on a climate zoning originally developed for thermal and energy efficiency provisions, clustering locations that present different moisture profiles. This paper investigates the suitability of the existing climate zones for hygrothermal risk indications by calculating the moisture index for several representative cites across Australia. Results reveal the current climate mapping to be unsuitable and too coarse to reliably provide robust indications for moisture management. Further, a new characterization of Australian climates for hygrothermal assessment purposes is proposed as a first step towards a better representation of moisture risks within the building code. [ABSTRACT FROM AUTHOR]

The energy generated by a solar photovoltaic (PV) system depends on uncontrollable factors, including weather conditions and solar irradiation, which leads to uncertainty in the power output. Forecast PV power generation is vital to improve grid stability and balance the energy supply and demand. This study aims to predict hourly day-ahead PV power generation by applying Temporal Fusion Transformer (TFT), a new attention-based architecture that incorporates an interpretable explanation of temporal dynamics and high-performance forecasting over multiple horizons. The proposed forecasting model has been trained and tested using data from six different facilities located in Germany and Australia. The results have been compared with other algorithms like Auto Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Multi-Layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost), using statistical error indicators. The use of TFT has been shown to be more accurate than the rest of the algorithms to forecast PV generation in the aforementioned facilities. [ABSTRACT FROM AUTHOR]

• Bottom-up estimation tool to develop electric public transport (EPT) charging loads. • Uses GTFS public data and can be scaled to hundreds of cities/states globally. • EPT could add 17% to critical peak demand on some distribution zone substations. Battery electrified public transit (BEPT), including buses and passenger ferries, is a promising solution to transport-related climate emissions and urban air pollution, but introduces potentially challenging large, coincident demand in the low-voltage distribution network. This paper presents a tool for estimating the energy and charging demand of electrified public transit using public data that is available in over 150 cities/states globally and demonstrates it in two case studies. The tool applies heuristic vehicle scheduling to existing public General Transit Feed Specification data to model the charging profiles and electricity consumption of BEPT for various charging regimes across different geographical scales. For the case study of New South Wales, Australia, the impacts of BEPT are most significant at the low-voltage network, where adding a battery-electric bus depot was found to increase the annual critical peak demand at the local zone substation in Summer by up to 17% while exacerbating demand during the daily evening peak period by 20–30%. Across the entire state of NSW, a full transition to BEPT increases annual electricity consumption by 1.28–1.34% and peak daily demand by 1–3%. [ABSTRACT FROM AUTHOR]

From the perspective of energy providers, accurate short-term load forecasting plays a significant role in the energy generation plan, efficient energy distribution process and electricity price strategy optimisation. However, it is hard to achieve a satisfactory result because the historical data is irregular, non-smooth, non-linear and noisy. To handle these challenges, in this work, we introduce a novel model based on the Transformer network to provide an accurate day-ahead load forecasting service. Our model contains a similar day selection approach involving the LightGBM and k-means algorithms. Compared to the traditional RNN-based model, our proposed model can avoid falling into the local minimum and outperforming the global search. To evaluate the performance of our proposed model, we set up a series of simulation experiments based on the energy consumption data in Australia. The performance of our model has an average MAPE (mean absolute percentage error) of 1.13, where RNN is 4.18, and LSTM is 1.93. [ABSTRACT FROM AUTHOR]

Aged care communities have been under the spotlight since the beginning of 2020. Energy is essential to ensure reliable operation and quality care provision in residential aged care communities (RAC). The aim of this study is to determine how RAC's yearly energy use and peak demand changed in Australia and what this might mean for RAC design, operation and energy asset investment and ultimately in the healthcare plan for elderly residents. Five years of electricity demand data from four case study RACs in the same climate zone are analyzed. Statistical tools are used to analyze the data, and a clustering algorithm is used to identify typical demand profiles. A number of energy key performance indicators (KPIs) are evaluated, highlighting their respective benefits and limitations. The results show an average 8% reduction for yearly energy use and 7% reduction for yearly peak demands in the COVID-19 year compared with the average of the previous four years. Typical demand profiles for the four communities were mostly lower in the pandemic year. Despite these results, the KPI analysis shows that, for these four communities, outdoor ambient temperature remains a very significant correlation factor for energy use. [ABSTRACT FROM AUTHOR]

A range of energy sources are used by households across the globe, including electricity, gas, solar, and wood. While there is a large body of international research aimed at understanding household energy use in general, very few studies have focused on the specific topic of residential firewood consumption. In Australia, empirical research to investigate and quantify residential firewood consumption is scarce and outdated, despite the importance of such research for better understanding overall household energy costs and carbon emissions. Unlike other power sources, such as electricity, gas and solar, firewood consumption is not systematically monitored or measured in an objective way, thereby making it difficult to obtain accurate data on the amount of firewood consumed in the residential sector. To address this data gap, the current study used survey data from a sample of 4844 households (including 1168 firewood users) to explore what socio-demographic, dwelling-related and behavioural factors were important for predicting the amount of firewood consumed by Australian households. The results revealed that a range of variables—including geographical location, dwelling type, the number of wood-fired appliances per household, and behavioural factors such as the purpose, timing, frequency, and duration of firewood use—were key determinants of residential firewood consumption in Australia, as self-reported by households. Together, these findings underscore the value of measuring not only geographical and location-based factors in household surveys, but also a range of socio-demographic, dwelling-related, and behavioural variables. By doing so, self-report data collected through surveys is likely to better predict the amount of residential firewood consumption reported (or estimated) by households. Overall, this study makes an important and timely contribution to the literature by demonstrating how social science methods such as household surveys can help improve current estimates of residential firewood consumption used to inform government policy, planning, and decision-making for the future. [ABSTRACT FROM AUTHOR]

Since existing residential buildings are a significant global contributor to energy consumption and greenhouse gas (GHG) emissions, any serious effort to reduce the actual energy and carbon emissions of the building sector should explicitly address the carbon mitigation challenges and opportunities in the building stock. This research investigates environmentally and economically sustainable retrofit methods to reduce the carbon footprint of existing residential buildings in the City of Greater Dandenong as a case study in Metropolitan Melbourne, Australia. By categorizing energy use into various building age brackets and dwelling types that align with changes in energy regulations, we identified various retrofit prototypes to achieve a targeted 6.5-star and 8-star energy efficiency rating (out of a maximum 10-star rating system). The corresponding operational energy savings through different retrofit options are examined while also considering the quantity of materials required for each option, along with their embodied energy and GHG emissions, thus allowing a more comprehensive lifecycle carbon analysis and exploration of their financial and environmental payback times. Results show that when buildings are upgraded with a combination of insulation and double-glazed windows, the environmental benefits rise faster than the financial benefits over a dwelling's lifecycle. The size or percentage of a particular dwelling type within the building stock and the remaining lifecycle period are found to be the most important factors influencing the payback periods. Retrofitting the older single detached dwellings shows the greatest potential for lifecycle energy and carbon savings in the case suburb. These findings provide households, industry and governments some guidance on how to contribute most effectively to reduce the carbon footprint of the residential building sector. [ABSTRACT FROM AUTHOR]

Green energy by PV systems reduces the dependence on fossil fuel-based power plants. Maximizing green energy to meet the demand reduces the burden on conventional power plants, hence lesser burning and greenhouse gases (GHG) emissions. For this purpose, this study draws a relationship between tracking schemes of the PV systems to GHG mitigation potential. The best fit location for detailed analyses is selected among the 15 most populous cities of Australia. The solar radiation potential is increased to 7.78 kWh/m2/d through dual axes tracking compared to 7.54, 6.82, 5.94, 5.73 kWh/m2/d through the one axis, azimuth based, fixed-tilted, and fixed-horizontal surface schemes, respectively. Through the dual axes tracking scheme, a 1 MW PV system per annum energy output avoids the burning of 796,065.3 L of gasoline, 4308.7 barrels of crude oil which is equal to the mitigation of 1852.7 tCO2 equivalent GHGs. Concisely, the PV system, through its green energy output, can avoid the release of greenhouse gases from fossil-fuel plants to tackle climate change more effectively. [ABSTRACT FROM AUTHOR]

Globally, buildings account for nearly 40% of the total primary energy consumption and are responsible for 20% of the total greenhouse gas emissions. Energy consumption in buildings is increasing with the increasing world population and improving standards of living. Current global warming conditions will inevitably impact building energy consumption. To address this issue, this report conducted a comprehensive study of the impact of climate change on residential building energy consumption. Using the methodology of morphing, the weather files were constructed based on the typical meteorological year (TMY) data and predicted data generated from eight typical global climate models (GCMs) for three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5) from 2020 to 2100. It was found that the most severe situation would occur in scenario RCP8.5, where the increase in temperature will reach 4.5 °C in eastern Australia from 2080–2099, which is 1 °C higher than that in other climate zones. With the construction of predicted weather files in 83 climate zones all across Australia, ten climate zones (cities)—ranging from heating-dominated to cooling-dominated regions—were selected as representative climate zones to illustrate the impact of climate change on heating and cooling energy consumption. The quantitative change in the energy requirements for space heating and cooling, along with the star rating, was simulated for two representative detached houses using the AccuRate software. It could be concluded that the RCP scenarios significantly affect the energy loads, which is consistent with changes in the ambient temperature. The heating load decreases for all climate zones, while the cooling load increases. Most regions in Australia will increase their energy consumption due to rising temperatures; however, the energy requirements of Adelaide and Perth would not change significantly, where the space heating and cooling loads are balanced due to decreasing heating and increasing cooling costs in most scenarios. The energy load in bigger houses will change more than that in smaller houses. Furthermore, Brisbane is the most sensitive region in terms of relative space energy changes, and Townsville appears to be the most sensitive area in terms of star rating change in this study. The impact of climate change on space building energy consumption in different climate zones should be considered in future design strategies due to the decades-long lifespans of Australian residential houses. [ABSTRACT FROM AUTHOR]