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An efficient use of solar energy production requires reliable forecast information on surface solar irradiance. This article aims at providing a Model Output Statistics (MOS) method of improving solar irradiance forecasts from Weather Research and Forecasting (WRF) model. The WRF model was used to produce one year of day ahead solar irradiance forecasts covering Reunion Island with an horizontal resolution of 3 km. These forecasts are refined with a Kalman filter using hourly high quality ground measurements. Determination of the relevant data inputs for the Kalman filter method is realized with a bias error analysis. The solar zenith angle and the clear sky index, among others, are used for this analysis. Accuracy of the method is evaluated with a comprehensive testing procedure using different error metrics. The Kalman filtering appears to be a viable method in order to improve the solar irradiance forecasting. Refereed/Peer-reviewed

Aim: The General Dynamic Model of island biogeography (GDM) explains patterns of species richness on volcanic hotspot archipelagos with respect to island age (T) and area (A), most commonly employing what we here call semi‐log ATT² and LogATT² models. These can be considered direct extensions of semi‐log and power law species‐area relationships (SARs), with a hump‐shaped function of T modelling drivers of biodiversity over and above area per se. We compare these and related models and investigate their conceptual and mathematical connections. Location: The Azores, Galapagos, Hawaii, Marquesas, Society Islands, Cape Verde Islands, Canary Islands and Aeolian archipelagos. Taxa: Vascular plants, birds, bryophytes, arthropods and molluscs. Methods: We gathered species richness data from databases and published sources, resulting in 34 datasets for various taxa. We used the Akaike information criterion corrected for small sample size (AICc) to compare the relative performance of models. We analysed the data at archipelago level and across archipelagos by using mixed effect models. We also calculated the relative time of expected peak diversity (Tpeak) from parameter estimates. Results: In almost three‐fourth of the cases the best model overall was one that contained only A or T rather than both predictors. There was greater variance between taxa than between archipelagos. Better dispersed taxa tended to reach peak diversity earlier. The most generally applicable of the models including T were the LogATT² and LogTT² models. These have important, previously unrecognized mathematical properties. Main Conclusions: SARs and species–time relationships should always be considered as potentially more parsimonious options. Considering the LogATT² model as a modification of the power law S = cAz, where a lognormal function in T replaces the constant, c allows the construction of an extended framework for the GDM with incorporation of additional predictors such as measures of environmental heterogeneity and application beyond hotspot archipelagos. Refereed/Peer-reviewed

Accurately forecasting the output of grid connected wind and solar systems is critical to increasing the overall penetration of renewables on the electrical network. This includes not only forecasting the expected level, but also putting error bounds on the forecast. The National Electricity Market (NEM) in Australia operates on a five minute basis. We used statistical forecasting tools to generate forecasts with prediction intervals, trialing them on one wind and one solar farm. In classical time series forecasting, construction of prediction intervals is rudimentary if the error variance is constant—Termed homoscedastic. However, if the variance changes—Either conditionally as with wind farms, or systematically because of diurnal effects as with solar farms—The task is much more complicated. The tools were trained on segments of historical data and then tested on data not used in the training. Results from the testing set showed good performance using metrics, including Coverage and Interval Score. The methods used can be adapted to various time scales for short term forecasting. Refereed/Peer-reviewed

Current and newly built buildings will inevitably experience the effects of climate change, therefore, the design and performance of these buildings should consider weather data that includes some of the effects of climate change, instead of only using historical weather data. However, climate change weather data suitable for buildings performance simulation are typically unavailable. This research presents a method to integrate climate change features into historical weather data to make suitable climate change weather data available for buildings performance simulation. The method separates hourly dry bulb temperature into three time series components to simplify the integration of three climate change features. The method adjusts the maximum and minimum monthly averages, the number of days with maximum temperature above a specified threshold, and the number of consecutive occurrences of days with maximum temperature above a specified threshold (heatwave). The research also presents the adjustment of monthly averages of global solar irradiation. Under the climate–changed weather conditions, the annual heating thermal energy decreases by 21%–22%, the annual cooling thermal energy increases by 29%–31%, and the combined heating and cooling thermal energy decreases by 4%–5% compared to the heating and cooling thermal energy under the current weather conditions. The results indicate that climate–changed weather data is necessary as historical weather data is insufficient for accurately assessing the energy performance of a building. Refereed/Peer-reviewed

At present, solar energy resources are becoming more and more popular as a substitute for conventional energy sources, in order to reduce greenhouse gas emissions. In order to further improve the utilization rate of solar energy resources, storage, as an important supplement, is more and more widely used. One of the most important research directions is to determine the necessary size of storage capacity by mathematic modeling. This paper will describe a new and efficient decision-making process to determine a series of storage capacities for a grid-connected PV system. The benefit of using a decision-making model is that it can easily determine the most appropriate storage capacity for the system. The paper will show here that the decision-making model will not only find the most effectiveness storage, but also find the relationship between storage capacities and the utilization rates of renewable energy. The case study shows that using 141.25 kWh storage increases the utilization rate of renewable energy in demand by 3.605%. And it would recover the storage cost by 4.66 years in Australia market. Refereed/Peer-reviewed

Privacy enhancing technologies (PETs) in mobile platforms typically restrict undesired information flow based on its sensitivity. However, sensitivity is often regarded as dichotomous and inflexible to the ever-changing contexts. Improving the effectiveness of PETs requires a better understanding of these contexts. In this paper, we examine the influence of contextual factors in users’ mobile usage based on Nissenbaum’s framework of contextual integrity. Specifically, we conducted a user study (n = 2889) to investigate the influence of relevance of information types on the willingness of disclosure towards typical groups of recipient. While the results suggest a significant relationship between information relevance (of different information) and willingness to disclose (to different recipients), closer examination reveals the relationship is not always clear-cut, and there is a potential influence of recipient. Therefore, incorporating the recipient factor can serve as a potential improvement to the existing approach in privacy management in the mobile device. Refereed/Peer-reviewed

There is an increasing demand for cooling cities because of its importance on human health and the quality of life in outdoor urban spaces. However, the development of methods in improving outdoor thermal comfort and zoning cities based on outdoor thermal comfort is still challenging. In this work, we propose a new approach to cities zoning from the lens of outdoor thermal comfort in the arid climate of the city of Mashhad, Iran, and investigate the impacts of urban form characteristics on pedestrian thermal comfort. The effects of complex urban form parameters including height to width (H/W) ratio, canyon orientation, tree canopy cover, and building surface materials on the thermal comfort of pedestrians were studied in the arid climate of Mashhad. Microclimate simulation and analysis is conducted in ENVI-met software, and ArcMap is used to calculate Mashhad urban heat islands. Path analysis in SPSS presents an urban form formulation, which predicts approximate outdoor thermal comfort condition in current and future urban context of Mashhad and other cities with the same climate. We finally demonstrate the use of our research method as an alternative method for all cities: urban heat island (UHI) zoning can be used as a substitute for urban form zonings based on outdoor thermal comfort, especially in large cities where data collection on urban form can be difficult due to limited time and resources. Refereed/Peer-reviewed

Solar radiation components assessment is a highly required activity for solar energy applications. Despite their importance, diffuse and direct solar radiation components are not available in many locations in the world. This is specifically the case in the studied region due to high fiscal demands and technical limitations. In the present work, our main objective is to estimate the two components using global solar radiation components as the only measured input parameter. Machine learning (ML) techniques seem to be a good solution of such an estimation problem but the main issue of ML techniques is the need of long historical data of both desired outputs, direct and diffuse, to build the optimum model in the training phase. However, in the present study, we adopt the use of the Boland–Ridley–Lauret (BRL) model to deal with the problem of estimation of the direct and diffuse components from the global radiation. The adjusted BRL model was applied on two time scales, daily and hourly components. It was found that the estimated direct and diffuse solar radiation values by the adjusted BRL model are in favorable agreement with the measured data. Refereed/Peer-reviewed

In Australia, heatwaves are the deadliest natural hazard and a major driver of peak electricity demand. The disproportionately high peak demand increases electricity prices, causes occasional blackouts and exacerbates energy poverty, all of which limit one's ability to use air conditioning. Meanwhile, increased energy efficiency of dwellings may decrease their heat stress resistance. This paper challenges whether the current Australian Nationwide Energy Rating Scheme encourages heat stress resistance. Cooling consumption, peak cooling demand and the risk of indoor overheating were assessed for a typical single-storey home in Adelaide and Sydney. Design options between 6 and 8 stars, plus two traditional, energy-inefficient homes were simulated with the AccuRate energy simulation program. This paper demonstrates that the NatHERS does not directly encourage heat stress resistance in new homes and can even deliver buildings with worse heat resistance and increased reliance on air conditioning than traditional, energy-inefficient buildings. This overreliance on air conditioning can present a public health hazard, consequently, the integration of heat stress resistance in the Nationwide House Energy Rating Scheme is recommended. Recommendations for the analysis and assessment of heat stress resistance are provided. Refereed/Peer-reviewed

Photovoltaic (PV) solar power, which is considered as the most competitive clean energy source, contributes to a significant percentage of electricity production in many developed countries. However, accurate PV power forecasting is necessary due to its high variation that can be caused by several factors. Hence, the intermittent nature of PV production represents a major challenge to integrate PV systems into the electric grid. The scope of this paper deals with this issue through developing a new integrated PV power forecasting model. The proposed model is based on the use of a new decomposition methodology, named Iterative Filtering for decomposing PV power into different intrinsic functions (IMFs), then Extreme Learning Machine (ELM) is used as essence predictor. To this end, the proposed IF-ELM model is evaluated on three solar PV power plants installed at three different sites, with different climatic conditions. Direct and recursive IF-ELM methodologies are examined for multi-step ahead forecasting in a very short time-scale (up to 60 min). Overall, the forecasting results show high precision performance for the studied forecasting horizons- in terms of different statistical metrics compared to stand-alone models. Also, the proposed IF method shows its high performance when compared to the recently developed decomposition method. complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) in improving the forecasting accuracy of a single model. Forecasting results with the IF-ELM model led to an error in nRMSE that is less than 10% and a Correlation Coefficient greater than 98% over all forecasting horizons. Refereed/Peer-reviewed

Electrical energy storage can be used to store excess power generated by domestic rooftop PV systems, rather than exporting it to the grid and then buying back energy at a higher price. We have used one-minute PV generation and electrical load data from thirty-eight low-energy homes to simulate the operation of energy storage, and to calculate the impact on the amount and cost of imported electricity. The payback period for energy storage systems depends on factors including the cost of energy storage, the cost of electricity, the price paid for exported energy, the power generated by the PV system, and how and when energy is used by the household. We calculate the payback period for various configurations. Decreasing feed-in tariffs and the decreasing cost of energy storage will lead to an uptake of energy storage system over the next few years. While storage can be used to reduce household electricity cost, it does not lead directly to reductions in CO 2 emissions. However, household energy storage will enable greater use of rooftop PV, and ultimately can be used to match household demand to variable supply from local and centralised renewable energy sources. Refereed/Peer-reviewed

Heatwaves have been subject to significant attention in Australia and globally due to their negative impacts on the ecosystem, infrastructure, human health and social life. Measures to increase resilience to heatwaves, however, are mostly isolated in different disciplines. This paper proposes a framework integrating urban and infrastructure planning, building design, public health and social research to comprehensively assess heat stress resilience. The proposed framework can assist decision makers in the evaluation of different policy changes addressing heat stress resilience and contribute to more comprehensive and effective heatwave management. An online survey was undertaken with a representative sample (N = 393) from Adelaide, South Australia, to explore heat stress resistance of the built environment, adaptation and heat-related, self-reported health problems. The study established the magnitudes of the secondary negative impacts of heatwaves on public health and daily routines. The findings identify a low level of resistance to heat stress by the built environment. It was concluded that community education along with focused building and planning regulations has the potential to significantly increase heat stress resilience. Refereed/Peer-reviewed

The current deep concerns on energy independence and global society's security at the face of climate change have empowered the new "green energy" paradigm and led to a rapid development of new methodology for modeling sustainable energy resources. However, clean renewables such as wind and solar energies are inherently intermittent, and their integration into a electric power grid require accurate and reliable estimation of uncertainties. And, if probabilistic forecasting of wind power is generally well developed, probabilistic forecasting of solar power is still in its infancy. In this paper we propose a new data-driven method for constructing a full predictive density of solar radiance based on a nonparametric bootstrap. We illustrate utility of the new bootstrapped statistical ensembles for probabilistic one-hour ahead forecasting in Mildura, Australia. We show that the new approach delivers sharp and calibrated ensembles of one-hour forecasts, and is computationally inexpensive and easily tractable. Refereed/Peer-reviewed

Although heatwave-related excess mortality and morbidity have been widely studied, results are not comparable spatially and often longitudinally because of different heatwave definitions applied. The excess heat factor (EHF) quantifies heatwave intensity relative to the local climate, enabling cross-regional comparisons. Previous studies have shown a strong relationship between EHFs and daily mortality during severe heatwaves. An extensive study about the relationship between EHFs and daily morbidity compared to the currently applied heatwave thresholds in Adelaide has not yet been undertaken. This paper analyzes the association of EHFs with daily morbidity between 2008 and 2014 in the Adelaide metropolitan region, South Australia, and probes three different approaches to calculate the EHF. The EHF is found to differentiate days with heatwave-related excess morbidity significantly better than other widely used weather parameters, resulting in fewer days per year with heatwave alerts than using previously proposed methods. The volume of excess morbidity can be predicted by the EHF more reliably with a model proposed for the SA Ambulance Service to support their heatwave preparation plan. Refereed/Peer-reviewed

With the recent rapid increase in the use of roof top photovoltaic solar systems worldwide,and also, more recently, the dramatic escalation in building grid connected solar farms, especially in Australia, the need for more accurate methods of very short-term forecasting has become a focus of research. The International Energy Agency Tasks 46 and 16 have brought together groups of experts to further this research. In Australia, the Australian Renewable Energy Agency is funding consortia to improve the five minute forecasting of solar farm output, as this is the time scale of the electricity market. The first step in forecasting of either solar radiation or output from solar farms requires there presentation of the inherent seasonality. One can characterise the seasonality in climate variables by using either a multiplicative or additive modelling approach. The multiplicative approach with respect to solar radiation can be done by calculating the clearness index, or alternatively estimating the clear sky index. The clearness index is defined as the division of the global solar radiation by the extraterrestrial radiation, a quantity determined only via astronomical formulae. To form the clear sky index one divides the global radiation by a clear sky model. For additive de-seasoning, one subtracts some form of a mean function from the solar radiation. That function could be simply the long term average at the time steps involved, or more formally the addition of terms involving a basis of the function space. An appropriate way to perform this operation is by using a Fourier series set of basis functions. This article will show that for various reasons the additive approach is superior.Also, the differences between the representation for solar energy versus solar farm output will be demonstrated. Finally, there is a short description of the subsequent steps in short-term forecasting. Refereed/Peer-reviewed

In the last decade, millions of dollars have been being invested in solar energy plants in regions prone to dust storms. Dust has a direct effect on the efficiency of solar energy plants by soiling and scattering of solar radiation. Hence it becomes necessary to analyse the potential of dust effect on Photovoltaic (PV) system performance as more solar energy is fed into national electricity grids. Dust impact on PV systems depends on how far the site is from dust sources in addition to wind speed and humidity. These factors in turn determine the soiling and scattering effect. Case studies are presented here to clarify the potential of dust impact from the main active dust sources in Western Asia. The results revealed a wide range of variability where the power output changed from -37% compared with the day before the impact of a 48 hour old dust cloud in one site, to +14.4% in another site. The case studies showed the importance of wind in cleaning the PV during dust events. It also shows the role of humidity in impeding self-cleaning. It turns out that a PV site influenced by dry desert air is more suitable for self-cleaning PV systems, compared with a site influenced by humid air. This outcome might help PV system investors to select a PV plant's location to be as far from the sea or extensive water sources as possible in regions prone to dust storms such as Western Asia. Refereed/Peer-reviewed

For design and simulation of solar energy systems, quality information about all components of solar irradiance is crucial. In cases when only global irradiance measurements are available, separation models are a useful method to estimate DNI and diffuse irradiation. Several of such models have been developed since the 1960s, most of them aiming to deliver estimates in hourly resolution. For higher data resolution, such as in minute data, those models are not able to describe fast transient and cloud enhancement phenomena commonly observed in data with smaller time-steps. This paper proposes an adaptation of the BRL separation model, making it capable of delivering more precise irradiance estimates for higher resolution data. Two models result from this adaptation: one for Brazil and other for Australia. The proposed models yield a more precise DNI and diffuse fraction estimates to their respective countries, compared to other separation models commonly used in the technical literature. For example, using the recommended Combined Performance Index (CPI) as a single statistical indicator, the proposed model yields DNI estimates with CPI from 230 to 350% for Australia, and from 270 to 800% for Brazil, while the Engerer model, recently recommended as a ''quasi-universal" 1-min separation model, yields DNI estimates with CPI from 500 to 700% for Australia, and from 600 to 1800% for Brazil. Refereed/Peer-reviewed

New electricity generation technologies are often assessed using simple metrics such as average return, break-even energy prices or levelised cost of electricity. These simple metrics do not always capture the full economic value of a technology, particularly those that can react quickly and efficiently to changes in demand. In a wholesale spot market, opportunities exist to capture greater revenues, as currently achieved by peak power plants. This study demonstrates the use of two complementary methods to determine how storage should be operated to maximise revenue. First, the authors formulate and solve the problem as a linear program. The results indicate that there are distinct control modes. They then use Pontryagin's principle to confirm that the optimal operating strategy has three distinct control modes: (i) store all collected power, without generating, (ii) generate using collected power only and (iii) generate at maximum capacity using both collected and stored power. The mode that should be used at any instant depends only on the spot price of electricity relative to a pair of critical prices. These critical prices depend on the total energy that will be collected and the turnaround efficiency of the storage system Refereed/Peer-reviewed

As the frequency and intensity of heatwaves are growing in Australia, strategies to combat heat are becoming more vital. Cities are exposed to urban heat islands (UHIs) due to excess urbanisation. In this study, a definition of urban heatwave (UHW) is conceptualised to investigate the combined impacts of heatwaves and UHIs. To quantify the negative impacts of UHW, indicators-such as excess morbidity, electricity and water consumption-are considered. The intensity of UHWs is calculated using the unit of excess heat factor (EHF), developed by the Australian Bureau of Meteorology. EHF enables the comparability of UHWs in different geographical locations. Using the indicators and the intensity of UHWs, a calculation method to quantify heatwave resilience at a precincts scale is proposed. The study summarises the assumed influential factors of precinct heatwave resilience based on the existing literature and propose a "cool retrofitting toolkit" (CRT). CRT creates the framework to assess the adaptation to and mitigation of UHWs available to retrofit existing precincts, and to evaluate potential retrofitting strategies in terms of energy and carbon efficiency, financial affordability and perceived acceptability by population. This study illuminates the importance of climate, function, built environment and population characteristics-conscious retrofitting. Refereed/Peer-reviewed

The proliferation of non-scheduled generation from renewable electrical energy sources such concentrated solar power (CSP) presents a need for enabling scheduled generation by incorporating energy storage, either via directly coupled Thermal Energy Storage (TES) or Electrical Storage Systems (ESS) distributed within the electrical network or grid. The challenges for 100% renewable energy generation are: to minimise capitalisation cost and to maximise energy dispatch capacity. The aims of this review article are twofold: to review storage technologies and to survey the most appropriate optimisation techniques to determine optimal operation and size of storage of a system to operate in the Australian National Energy Market (NEM). Storage technologies are reviewed to establish indicative characterisations of energy density, conversion efficiency, charge/discharge rates and costings. A partitioning of optimisation techniques based on methods most appropriate for various time scales is performed: from “whole of year”, seasonal, monthly, weekly and daily averaging to those best suited matching the NEM bid timing of five minute dispatch bidding, averaged on the half hour as the trading settlement spot price. Finally, a selection of the most promising research directions and methods to determine the optimal operation and sizing of storage for renewables in the grid is presented.

Understanding the relationships between the Australian economic system and waste generation from intermediate sectors and households is a prerequisite for planning and implementing waste management strategies at a national scale. Data of waste generation accounts link to those of national economic accounts. However, in Australia, some years’ data are absent and so these links cannot be made. To rectify this data gap, this paper interpolates and extrapolates the Australian input-output table (IOT) of 2010–2011. Waste input-output (WIO) analysis is then used to assess the effects of the Australian economy on waste generation and treatment between 2009–2010 and 2010–2011. Analysis indicated that the result of interpolation was more reasonable than that of extrapolation, and the interpolation of the Australian IOT of 2010–2011 can be applicable. This comparative analysis of the time series data in WIO model has identified that: (1) per million $AUD of output of the Construction sector generated the most amount of direct and total waste during the period; (2) the relationships between the development of Australian economy and waste generation illustrate that the Australian economy is currently a traditional linear economy; (3) the effectiveness of waste-related policies are shown by the growth of the sums of direct and total effects of intermediate sectors on the Recovery sector; and (4) the amount of waste generated by households increased sharply over the two years. The physical flows of waste footprint show details of waste generation and treatment in the Australian economic system. The information provided in this paper is beneficial to formulate tailor-made policies for waste management in Australia. Refereed/Peer-reviewed

This study investigates the land surface temperature (LST) distribution from thermal infrared data for analyzing the characteristics of surface coverage using the Vegetation-Impervious-Soil (VIS) approach. A set of ten images, obtained from Landsat-5 Thematic Mapper, between 2001 and 2010, were used to study the urban environmental conditions of 47 neighborhoods of Porto Alegre city, Brazil. Porto Alegre has had the smallest population growth rate of all 27 state capitals in the last two decades in Brazil, with an increase of 11.55% in inhabitants from 1.263 million in 1991 to 1.409 million in 2010. We applied the environmental Kuznets curve (EKC) theory in order to test the influence of the economically-related scenario on the spatial nature of social-environmental arrangement of the city at neighborhood scale. Our results suggest that the economically-related scenario exerts a non-negligible influence on the physically driven characteristics of the urban environmental conditions as predicted by EKC theory. The linear inverse correlation R2 between household income (HI) and LST is 0.36 and has shown to be comparable to all other studied variables. Future research may investigate the relation between other economically-related indicators to specific land surface characteristics. Refereed/Peer-reviewed

Outdoor thermal discomfort pushes citizens into air-conditioned buildings and causes increased demand for water and electricity in the majority of Australian urban heat islands. Citizens’ spatial and activity preferences during heat stress conditions are under investigation in this paper. Citizens’ outdoor activity choices in different thermal environments were surveyed in Adelaide from September 2013 to April 2014. The post-activity questionnaire survey of outdoor activities in Adelaide indicates that necessary, optional and social activities decreased during outdoor heat stress more than any other thermal conditions. Outdoor activities were chosen the most in neutral and warm thermal environments. Outdoor activity choices were affected significantly by the urban microclimate parameter of solar radiation. Tree canopy, shading (from buildings or temporary elements) and water features were the most attractive public space features for outdoor participants during heat stress conditions in Adelaide. Meanwhile, essential shopping and dining facilities and social events affect citizens’ outdoor activity choices during heat stress conditions. Thus, increased green infrastructures and supportive land uses are a prerequisite of urban transformation towards high-performance built environment in the context of climate change. Refereed/Peer-reviewed

Quality data regarding direct and diffuse solar irradiation is crucial for the proper design and simulation of solar systems. This information, however, is not available for the entire Brazilian territory. However, hourly measurements of global irradiation for more than seven hundred stations over the territory are available. Several mathematical models have been developed over the past few decades aiming to deliver estimations of solar irradiation components when only measurement of global irradiation is available. In order to provide reliable estimates of diffuse and direct radiation in Brazil, the recently presented Boland–Ridley–Laurent (BRL) model is adjusted to the particular features of Brazilian climate data, developing adjusted BRL models on minute and hourly bases. The model is adjusted using global, diffuse and direct solar irradiation measurements at nine stations, which are maintained by INPE in the frame of the SONDA project. The methodology for processing and analyzing the quality of the data-sets and the procedures to build the adjusted BRL model is thoroughly described. The error indicators show that the adjusted BRL model performs better or similarly to the original one, for both diffuse and DNI estimates calculated for each analyzed Brazilian station. For instance, the original BRL model diffuse fraction estimates have MeAPE errors ranging from 16% to 51%, while the adjusted BRL model gives errors from 9% to 26%. Regarding the comparison between the minute and hourly adjusted models, it can be concluded that both performed similarly, indicating that the logistic behavior of the original BRL model is well suited to make estimates in sub-hourly data sets. Based on the results, the proposed adjusted model can be used to provide reliable estimates of the distribution of direct and diffuse irradiation, and therefore, can help to properly design and reduce the risks associated to solar energy systems. Refereed/Peer-reviewed

Rooftop solar photovoltaic panels, household electrical energy storage (batteries), home energy management, interval metering and new tariffs will change the way that households use electricity from the grid. Distributed storage can also give electricity retailers the ability to shift loads in response to changes in the wholesale price of electricity and constraints on the distribution network. What is the ideal mix of photovoltaic, storage and tariff for a customer? What is the value of these technologies to customers and to electricity retailers? Refereed/Peer-reviewed

Compiling, deploying and utilising large-scale databases that integrate environmental and economic data have traditionally been labour- and cost-intensive processes, hindered by the large amount of disparate and misaligned data that must be collected and harmonised. The Australian Industrial Ecology Virtual Laboratory (IELab) is a novel, collaborative approach to compiling large-scale environmentally extended multi-region input-output (MRIO) models.The utility of the IELab product is greatly enhanced by avoiding the need to lock in an MRIO structure at the time the MRIO system is developed. The IELab advances the idea of the "mother-daughter" construction principle, whereby a regionally and sectorally very detailed "mother" table is set up, from which "daughter" tables are derived to suit specific research questions. By introducing a third tier - the "root classification" - IELab users are able to define their own mother-MRIO configuration, at no additional cost in terms of data handling. Customised mother-MRIOs can then be built, which maximise disaggregation in aspects that are useful to a family of research questions.The second innovation in the IELab system is to provide a highly automated collaborative research platform in a cloud-computing environment, greatly expediting workflows and making these computational benefits accessible to all users.Combining these two aspects realises many benefits. The collaborative nature of the IELab development project allows significant savings in resources. Timely deployment is possible by coupling automation procedures with the comprehensive input from multiple teams. User-defined MRIO tables, coupled with high performance computing, mean that MRIO analysis will be useful and accessible for a great many more research applications than would otherwise be possible. By ensuring that a common set of analytical tools such as for hybrid life-cycle assessment is adopted, the IELab will facilitate the harmonisation of fragmented, dispersed and misaligned raw data for the benefit of all interested parties. © 2014 Elsevier B.V.

Heatwaves are the most dangerous natural hazard to health in Australia. The frequency and intensity of heatwaves will increase due to climate change and urban heat island effects in cities, aggravating the negative impacts of heatwaves. Two approaches exist to develop population heat stress resilience. Firstly, the most vulnerable social groups can be identified and public health services can prepare for the increased morbidity. Secondly, the population level of adaptation and the heat stress resistance of the built environment can be increased. The evaluation of these measures and their efficiencies has been fragmented across research disciplines. This study explored the relationships between the elements of heat stress resilience and their potential demographic and housing drivers and barriers. The responses of a representative online survey (N=393) about heat stress resilience at home and work from Adelaide, South Australia were analysed. The empirical findings demonstrate that heat stress resistant buildings increased adaptation capacity and decreased the number of health problems. Air-conditioning increased dependence upon it, limited passive adaptation and only people living in homes with whole-house air-conditioning had less health problems during heatwaves. Tenants and respondents with pre-existing health conditions were the most vulnerable, particularly as those with health conditions were not aware of their vulnerability. The introduction of an Energy Performance Certificate is proposed and discussed as an effective incentive to increase the heat stress resistance of and the general knowledge about the built environment. Refereed/Peer-reviewed

In publicly available waste reports only the totals of waste produced for municipal, or industry waste typically feature. The types of waste generated and the generating industry sector are usually omitted. We propose the direct inputs waste estimation methodology to create a detailed estimate of municipal solid waste and industrial solid waste for an economy (including sectoral and waste type disaggregation) using a top-down estimation methodology that links the aforementioned limited publicly available waste data with an input-output table's direct inputs (A) matrix. We then provide an application of the direct inputs waste estimation methodology upon the 2008 waste generation of Australia resulting in a 344 industry sector and 14 waste type data set. The resulting estimation gives unique insights into Australian waste generation; including the large C&I tonnages of waste estimated to be produced from the Service sectors such as the Education, Hospitality, and Health sectors as well as the large amount of food waste produced throughout the economy Refereed/Peer-reviewed

Due its intermittency, the large-scale integration of solar energy into electricity grids is an issue and more specifically in an insular context. Thus, forecasting the output of solar energy is a key feature to efficiently manage the supply-demand balance. In this paper, three short term forecasting procedures are applied to island locations in order to see how they perform in situations that are potentially more volatile than continental locations. Two continental locations, one coastal and one inland are chosen for comparison. At the two time scales studied, ten minute and hourly, the island locations prove to be more difficult to forecast, as shown by larger forecast errors. It is found that the three methods, one purely statistical combining Fourier series plus linear ARMA models, one combining clear sky index models plus neural net models, and a third using a clear sky index plus ARMA, give similar forecasting results. It is also suggested that there is great potential of merging modelling approaches on different horizons. Refereed/Peer-reviewed

The frequency and intensity of urban heatwaves (UHWs) have been growing worldwide due to climate change and the exacerbating effects of urban heat islands (UHIEs). UHWs have many negative impacts, including excess negative health outcomes (e.g.morbidity), energy (consumption and peak demand) and water consumption. Most studies have evaluated these impacts separately even though there is an interplay between them. The study assessed the daily excess morbidity, energy demand and consumption,and water supply in the Adelaide metropolitan region during heatwaves, between January 2008 and March 2014. The assessment quantifies the thresholds and the increase in each impact relative to temperature increase. The demonstrated negative impacts on public health, and energy and water resources, potentially exacerbated by UHIEs, justify the importance of interdisciplinary research and integrated policy changes on the mitigation of and adaptation to heatwaves. Refereed/Peer-reviewed

With ever increasing demand for electricity and the huge potential of renewable energy, an increasing number of renewable-energy sources are being used to generate electricity. However, due to the intermittency of renewable-energy generation, many researchers try to overcome the variable nature of renewable energy. A hybrid renewable-energy system is one possible way to introduce smoothing of the supply. Many hybrid renewable-energy studies focus on system optimization and management. This paper mainly researches the performance prediction accuracy of a hybrid solar and wind system. Through a mixed autoregressive and dynamical system model, we test the predictability of the hybrid system and compare it with individual solar and wind series forecasting. After error analysis, the predictability of the hybrid system shows a better performance than solar or wind for Adelaide global solar radiation and Starfish Hill wind farm data. The prediction errors were reduced by 13% to more than 30% according to various error analyses. This result indicates an advantage of the hybrid solar and wind system compared to solar and wind systems taken individually. Refereed/Peer-reviewed

International audience; For some locations both global and diffuse solar radiation are measured. However, for many locations, only global radiation is measured, or inferred from satellite data. For modelling solar energy applications, the amount of radiation on a tilted surface is needed. Since only the direct component on a tilted surface can be calculated from direct on some other plane using trigonometry, we need to have diffuse radiation on the horizontal plane available. There are regression relationships for estimating the diffuse on a tilted surface from diffuse on the horizontal. Models for estimating the diffuse on the horizontal from horizontal global that have been developed in Europe or North America have proved to be inadequate for Australia [13]. Boland et al. [2] developed a validated model for Australian conditions. Boland et al. [3] detailed our recent advances in developing the theoretical framework for the use of the logistic function instead of piecewise linear or simple nonlinear functions and was the first step in identifying the means for developing a generic model for estimating diffuse from global and other predictors. We have developed a multiple predictor model, which is much simpler than previous models, and uses hourly clearness index, daily clearness index, solar altitude, apparent solar time and a measure of persistence of global radiation level as predictors. This model performs marginally better than currently used models for locations in the Northern Hemisphere and substantially better for Southern Hemisphere locations. We suggest it can be used as a universal model.

For some locations both global and diffuse solar radiation are measured. However, for many locations, only global is measured, or inferred from satellite data. For modelling solar energy applications, the amount of radiation on a tilted surface is needed. Since only the direct component on a tilted surface can be calculated from trigonometry, we need to have diffuse on the horizontal available. There are regression relationships for estimating the diffuse on a tilted surface from diffuse on the horizontal. Models for estimating the diffuse radiation on the horizontal from horizontal global that have been developed in Europe or North America have proved to be inadequate for Australia [Spencer JW. A comparison of methods for estimating hourly diffuse solar radiation from global solar radiation. Sol Energy 1982; 29(1): 19–32]. Boland et al. [Modelling the diffuse fraction of global solar radiation on a horizontal surface. Environmetrics 2001; 12: 103–16] developed a validated model for Australian conditions. We detail our recent advances in developing the theoretical framework for the approach reported therein, particularly the use of the logistic function instead of piecewise linear or simple nonlinear functions. Additionally, we have also constructed a method, using quadratic programming, for identifying values that are likely to be erroneous. This allows us to eliminate outliers in diffuse radiation values, the data most prone to errors in measurement.

We have developed a model to generate synthetic sequences of half-hourly electricity demand. The generated sequences represent possible realisations of electricity load that could have occurred. Each of the components included in the model has a physical interpretation. These components are yearly and daily seasonality which were modelled using Fourier series, weekly seasonality modelled with dummy variables, and the relationship with current temperature described by polynomial functions of temperature. Finally the stochastic component was modelled with autoregressive moving average (ARMA) processes. These synthetic sequences were developed for two purposes. The first one is to use them as input data in market simulation software. The second one is to build probability distributions of the outputs to calculate probabilistic forecasts. As an application several summers of half-hourly electricity demand were generated and from them the value of demand that is not expected to be exceeded more than once in 10 years was calculated.

In this paper we investigate the economic and environmental efficiency of charities and NGO’s “rescuing” food waste, using a 2008 case study of food rescue organisations in Australia. We quantify the tonnages, costs, and environmental impact of food rescued, and then compare food rescue to other food waste disposal methods composting and landfill. To our knowledge this is the first manuscript to comprehend the psychical flows of charity within an Input-Output framework—treating the charity donations as a waste product. We found that 18,105 tonnes of food waste was rescued, and calculate that food rescue operations generate approximately six kilograms of food waste per tonne of food rescued, at a cost of US $222 per tonne of food rescued. This a lower cost than purchasing a tonne of comparable edible food at market value. We also found that per US dollar spent on food rescue, edible food to the value of US$5.71 (1863 calories) was rescued. Likewise, every US dollar spent on food rescue redirected food that represented 6.6 m3 of embodied water, 40.13 MJ of embodied energy, and 7.5 kilograms of embodied greenhouse gasses (CO2 equivalents) from being sent to landfill or composting, and into mouths of the food insecure. We find that food rescue—though more economically costly than landfill or composting—is a lower cost method of obtaining food for the food insecure than direct purchasing. Refereed/Peer-reviewed

This paper uses input-output analysis to model the environmental impacts of the weekly food consumption of Australia's households sorted by income quintile in 2003. We found that weekly food consumption of the relatively better off households caused greater environmental burden than that of the less well-off household.Meat and bakery products/flour/cereals were the categories that contributed the largest share of environmental impacts in an average household's food consumption footprint: 58% water, 26% energy, 29% CO2, and 31% wasted materials.Per dollar spent, fruit and vegetables generated 0.0874m3 of water, 0.0055GJ of energy, 0.37kg of CO2, and 0.0114kg of wasted materials. This was lower than the per dollar impacts of bakery products (0.8482, 0.0067, 0.63, 0.0204), meat (0.3471, 0.0070, 0.65, 0.0203), dairy (0.0995, 0.0114, 0.98, 0.0192), and edible fats (0.2373, 0.0080, 0.70, 0.0165). Eating out and fast food consumption also had a low per dollar impact (0.1317, 0.0046, 0.38, 0.0141).We surmise that substituting fruit and vegetables at the expense of animal products, processed foods, and fats would decrease environmental impacts. Change at low income levels should be focused upon meat, bakery and dairy consumption, while higher incomes should be focused upon the consequences of eating food outside the home Refereed/Peer-reviewed

This paper reviews the current literature around the environmental impacts of dietary recommendations. The focus of the review is on collating evidence relating to environmental impacts of the dietary advice found in the World Health Organisation guidelines, and environmental impact literature: reducing the consumption of fat, reducing the consumption of meat-based protein and animal-based foods, and increasing the consumption of fruit and vegetables. The environmental impact of reducing dietary fat intake is unclear, although reducing consumption of the food category of edible fats and oils appears to have little impact. However most, but not all, studies support environmental benefits of a reduced consumption of animal-based foods and increased consumption of fruit and vegetables. In general, it appears that adhering to dietary guidelines reduces impact on the environment, but further study is required to examine the environmental impacts of animal-based foods, and fruit and vegetable intake in depth. Refereed/Peer-reviewed

International audience; Integration of solar energy into the electricity network is becoming essential because of its continually increasing growth in usage. An efficient use of the fluctuating energy output of photovoltaic (PV) systems requires reliable forecast information. In fact, this integration can offer a better quality of service if the solar irradiance variation can be predicted with great accuracy. This paper presents an in-depth review of the current methods used to forecast solar irradiance in order to facilitate selection of the appropriate forecast method according to needs. The study starts with a presentation of statistical approaches and techniques based on cloud images. Next nu-merical weather prediction or NWP models are detailed before discussing hybrid models. Finally, we give indications for future solar irradiance forecasting approaches dedicated to the management of small-scale insu-lar grids.

In the financial year 2011–2012, wind farms supplied 26% of South Australia’s electricity demand according to the Australian Energy Market Operator's report. This contribution has risen from zero in 2003. The operation of the electricity grid depends heavily on knowledge of the variability of supply. Wind farm output displays similar conditional volatility as financialmarket variables. In this paper, a new method of estimating wind farm output volatility on a 5-min time scale is developed through the use of higherfrequency wind farm output data. First, an autoregressive model for the high-frequency data is developed, and it is used to derive a volatility measure for 5-min data. The results are also true in certain general situations when the high-frequency data follow an autoregressive moving average process or exhibits long memory features. The methods described here are analogous to realised volatility measures used in financial series, except that wind farm output data are measured at uniform intervals, unlike random trading times for financial transactions. Refereed/Peer-reviewed

Electricity generation is vital in developed countries to power the many mechanical and electrical devices that people require. Unfortunately electricity generation is costly. Although electricity can be generated easily but not normally stored. Electricity generation is also difficult to manage because exact demand is unknown from one instant to the next. A number of services are required to manage fluctuations in electricity demand, and to protect the system when frequency falls below acceptable levels. A current approach is called automatic under frequency load shedding. This article proposes new methods for optimising this approach in New Zealand's power system. The core ideas were developed during the 2015 Maths and Industry Study Group in Brisbane, Australia. The problem was motivated by Transpower Limited, a company that manages New Zealand's power system and transports bulk electricity from where it is generated to where it is needed. The approaches developed in this article can be used in electrical power systems anywhere in the world. References Arroyo J. M. and Galiana F. D. Energy and reserve pricing in security and network constrained electricity markets. IEEE Transactions on Power Systems , 20(2):C634–C643, 2005. Burdett R. and Kozan, E. Performance profiling for predictive train schedules. Journal of Rail Transport Planning and Management , 4:C98–C114, 2014. doi:10.1016/j.jrtpm.2014.11.002. Hiscock J., Hiscock N. and Kennedy A. Advanced voltage control for networks with distributed generation. 19th International Conference on Electricity Distribution . Vienna, 21–24 May, 2007. Just S. and Weber C. Pricing of reserves. Energy Economics , 30:C3198–C3221, 2008. El-Khattam W., Bhattacharya K., Hegazy Y. and Salama M. M. A. Optimal investment planning for distributed generation in a competitive electricity market. IEEE Transactions On Power Systems , 19(3). Kusano N. New trends in protection relays and substation automation systems in Japan. Transmission and Distribution Conference and Exhibition 2002: Asia Pacific. IEEE/PES , 1, 624–628. Lopes J. A., Hatziargyriou N., Mutale J., Djapic P. and Jenkins, N. Integrating distributed generation into electric power systems: A review of drivers, challenges and opportunities. Electric Power Systems Research , 77, 1189–1203. Thomson M. Automatic voltage-control relays and embedded generation. Power Engineering Journal , April, 71–76. Wong S. and Fuller D. J. Pricing energy and reserves using stochastic optimization in an alternative electricity market. IEEE Transactions on Power Systems , 22(2), 631–638. Transpower AUFLS Summary Card. WL 13309 https://www.systemoperator.co.nz/sites/.../AUFLS_Summary_Card.pdf Transpower Electricity reserve technical requirements schedule–-ERTWG https://www.systemoperator.co.nz/sites/default/files/bulk-upload/documents/ERTWG%20Trial%20Procurement%20Schedule%20Guide.pdf AIMMS AIMMS 3.13 - AIMMS Modeling Guide - Integer Programming Tricks. http://www.aimms.com

While wind farm development has advanced at a great rate in Australia, driven by the Mandatory Renewable Energy Target, the high volatility of wind energy is still a big challenge to the operators of the electricity grid. It is, therefore, important to obtain accurate estimates of the volatility and hence the prediction of the wind farms' output so that wind energy can enter fully and reliably into the competitive electricity market. With the motive of getting an accurate measure of wind energy variability, and given the fact that high frequency data for the wind farm energy output is available at the level of ten second intervals, we develop a method to estimate the variance of the five minute wind energy output using the ten second data. This "five minute" dispatch interval is important for the Australian Electricity Market Operators. Our mathematical results are applicable not only to the wind farm output but also to a wide range of situations, where the high frequency time series data are serially correlated. Refereed/Peer-reviewed

ITT has developed and demonstrated a real-time airborne data management system that ingests, compresses, stores, and streams imagery and video data from sensors based on users' needs. The data management system was designed to be sensor agnostic, which was demonstrated when ITT quickly integrated several different cameras including an HD video camera, an IR video camera, and large framing cameras. The data is compressed in real-time using ITT's high-speed JPEG 2000 compression core and stored in the airborne unit. The data is then interactively served to users over downlink communication based on the users' requests. This system's capability was demonstrated in several test flights where data was collected from the sensors at 132 megapixels per second (1.5 gigabits per second), compressed, stored, and interactively served as regions of interest to multiple users over a 48 megabit/second communication link. This data management system is currently being incorporated into airborne systems for military and civil applications.

Intuitively one decomposes climate variables such as solar irradiation and ambient temperature into their deterministic and stochastic components. The deterministic component comprises a sum of contributions at various frequencies, thereby defining the climate of the location. The stochastic component comprises the fluctuations about this component, giving the day-to-day weather variations. Boland (Solar Energy 60(6) (1997) 359) shows that on a day-to-day basis both components must be included in building thermal performance simulations to give sensible results. It is shown in this paper that on an hour-to-hour basis the stochastic component may be disregarded in building thermal, solar process heat and photovoltaic system performance without significantly affecting the results. Since the hourly stochastic component of the solar irradiation is very difficult to model, this result greatly simplifies the construction of synthetic climate data sets.

Equity in resource allocation is central to the tenet of social justice in health care. The management of surgical waiting lists is of critical importance to clinicians, patients and regulators. In most hospital environments, the basic process has remained unchanged for decades. Patients are assigned to one of three urgency-related categories. Clinicians consequently administer three competing patient pools. The basis by which patients are selected for treatment may be difficult to define. The specific clinical circumstances of each patient are often unreported and may be unknown to those administering the list. Waiting list bias is also recognised. This may reflect clinician advocacy, pressure to meet category timeframe restrictions or perceived training requirements. In this environment, it is difficult to demonstrate propriety in care. We report the implementation of a pilot program to redesign waiting list management within a South Australian public hospital unit. This allows assemblage of patients into a single list. Overall priority is determined by balancing clinical acuity and waiting time. The determination of acuity takes into account both the primary category and the specific characteristics of each patient that are relevant to their intended procedure. Uniquely, the process is applicable to lists containing patients with dissimilar conditions. This paper reviews the limitations of current approaches in meeting reasonable community expectations. The principles and social justification underpinning this reform are introduced. Finally, the benefits offered by the program are discussed and interim results are reported. What is known about this topic? Current models for the management of hospital waiting lists have remained largely unchanged for several decades. Typically patients are allocated to urgent, semi-urgent and non-urgent categories of care. No methodology exists to systematically integrate these groups, or to account for specific patient factors. In this void, propriety in management is difficult to establish or defend. What does this paper add? A program is reported that unifies all categories of patients into a single prioritised waiting list. The order of patients is dynamic, and transparently reflects waiting time, category assignment and relevant individual patient factors. Uniquely, the program is applicable to lists containing patients with diverse clinical conditions. What are the implications for clinicians? Adoption of new technology is essential if reasonable community expectations in waiting list management are to be met. The current program provides unambiguous, defensible prioritisation of all patients awaiting care. The present reliance on individual managers is reduced, and the unique circumstances of each patient are recognised. We believe this approach affords significant benefit to patients, practitioners and regulators.

The progress from the last four years in solar energy resource assessment for Chile is reported, including measurements from a ground station network spanning from two to three years of data, and satellite estimations from the recently developed Chile-SR model including two full years of data. The model introduces different treatments for the meteorological variables and the effective cloud cover computations which allow estimation of the global horizontal irradiation on an hourly basis. The BRL model of diffuse radiation is then applied in order to estimate the diffuse fraction and diffuse irradiation, from which the Direct horizontal irradiation is then computed. Direct normal irradiation is computed by applying proper solar geometry corrections to the direct horizontal irradiation. The satellite estimation model was developed as an adaptation from Brazil-SR model, with an improved formulation for altitude corrected atmospheric parameters, and a novel formulation for calculating effective cloud covers while at the same time detecting and differentiating it from snow covers and salt lakes. The model is validated by comparison with ground station data. The results indicate that there are high radiation levels throughout the country. In particular, northern Chile is endowed with one of the highest solar resources in the world, although the resource variability is higher than previously thought. Refereed/Peer-reviewed

People’s views of landscape can have significant impact on their land use choices. This in turn can have climate implications. In the literature, there is significant attention paid to the effect of climate on landscape, but as well, there are more examples than one might expect of the effects of landscape change on climate. I present evidence of this from Western Australia and Africa, and some conjectured negative and positive effects in two locations in South Australia. I argue that the land use change affecting the landscape and thus affecting the climate emanates from one’s view of landscape. There are also some personal reflections of perceptions of landscape from someone who started life in a climate and landscape far removed from the South Australian environment within which he has chosen to live. Refereed/Peer-reviewed