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• Comparison of day-ahead deterministic forecasts of solar irradiation and of production of a large-scale PV plant. • Use of Numerical Weather Predictions, post-processing methods and benchmark models. • Optimization of the operation of an energy storage system associated to a PV farm using the solar forecasts and a receding horizon approach. • Evaluation of the economic gain brought by the forecasts in order to increase the revenue offered by the energy arbitrage of the Australian electricity market. • Highlight of relationships between quality and economic value of solar forecasts. During the last decade, numerous solar forecasting tools have been developed to predict the energy generation of photovoltaic (PV) farms. The quality of solar forecasts is assessed by comparing predictions with measured solar data. However, this methodology does not consider the added value of the forecasts for their applications. As a consequence, what value could be given to the improvement of forecasts considering this evaluation framework? To answer this question, this work compares the value of different operational solar forecasts for a specific application. The aim is to look for relationships between the economic value and the error metrics defined to evaluate the forecast quality. A new generation of large-scale PV plants integrates ESS. The aim is to add flexibility to the injection of the production into the grid and thus to maximize the profit by taking advantage of the possibilities offered by the electricity market, such as energy arbitrage. To optimize the operation of these specific ESS, forecasting of the solar production is of paramount importance. The study case considered in this work is a large-scale PV farm of several megawatts associated with Li-ion batteries in the Australian energy market context. For this specific case study, the results show that the metrics used to evaluate the forecast quality based on the mean absolute error (MAE) have an almost linear relationship with the economic gain brought by applying the forecast. More precisely, an improvement of 1% point in MAE results approximately in an increase of 2% points in economical gain. [ABSTRACT FROM AUTHOR]

• Comprehensive review of urban planning policies on residential tree planting inclusion. • Identification of policy gaps regarding dominant tree planting configuration parameters. • Summarised tree arrangement technical guidelines in medium–density residential developments. • Understanding the asset of residential trees strengthening Australian suburban development policies. • Development of a general policy framework in arranging residential trees. Tree canopy loss in residential zones is a current Australian planning challenge in delivering liveable, heat–resilient urban developments. It is time for local governments to prioritise residential tree–planting programs on private properties. The best strategic practice is to endorse, optimise and protect a tree inclusion framework, especially for limited private green spaces, associating them with urban development policies. This paper examines residential tree inclusion opportunities in Australian design and planning policies at national and state levels, highlighting dominant tree planting configuration parameters. Then, it evaluates the Optimal Residential Tree arrangement (ORTa), combining site-specific tree policy requirements with building-integrated vegetation simulations for the Köppen Mediterranean climate classification. The ORTa offers technical guidelines, featuring three strategic outcomes based on property orientation, planting configuration, adequate deep soil level, and dwelling types. The paper also recommends a 4-step ORTa policy implementation process to ensure Local goverments' success in greener medium-density development. This process calls for addressing prior policy deficiencies, endorse Local Government green assets alongside the existing scattered residential tree assets and ground soil areas, supporting collaboration among planning, building and community systems and mandating residential tree inclusion for a maximum energy efficient benefit. Implementing ORTa encourages biodiversity contribution and water-sensitive urban designs while achieving urban tree canopy targets. [ABSTRACT FROM AUTHOR]

Grain boundary energies in different elements are correlated. The proportional scaling constants relating the energies of crystallography-equivalent boundaries in any two f.c.c. elements are nearly constant, with the notable exception of aluminum where these constants are known to vary significantly. However, the origins of the exceptional behavior of aluminum are not understood. Previously, we reported that for fcc metals there is a preference for tilt boundaries to shift their tilt axis across the (1 1 ¯ 0) plane towards [112] and to ultimately form low energy [112] core shifted boundaries (CSBs). By comparing grain boundary energies in copper and aluminum with different tilt axes in (1 1 ¯ 0) plane, we now report the existence of a well-defined scaling behavior for the case of low angle boundaries. In contrast, the scaling constant for high angle boundaries is essentially fixed regardless of their tilt axis shift. This results in a gradual change in the scaling constants from low angle to high angle boundaries, which is responsible for the apparent exceptional scaling behavior found in aluminum. An analysis of structure evolution during core shifting points to the significance of boundary-core dissociation, a form of correlated relaxation of individual atoms at boundaries, in controlling the scaling of boundary energies. [Display omitted] [ABSTRACT FROM AUTHOR]

Highlights • The BEKK and DCC econometric models of VAR-mGARCH type are studied on wind speed data. • These are spatio-temporal models which allow for cross-volatility modeling. • The models are studied in their data generation and forecasting aspects. • Their volatility forecasting ability is used in an energy portfolio control problem. Abstract This paper evaluates the application of a family of VAR-mGARCH (Vector AutoRegressive with multivariate AutoRegressive Conditional Heteroskedasticity) volatility models to the problem of modeling (i.e. generating correct in-sample scenarios) and forecasting in a probabilistic way three univariate but mutually dependent wind speed hourly series. The evaluation starts from the consideration that optimal modeling and optimal forecasting are not necessarily attained by the same models, and that they are assessed in different ways. The proposed VAR-mGARCH family, originated in Finance, consists of the VAR-BEKK (VAR - Baba, Engle, Kraft and Kroner) model and the VAR-DCC (VAR - Dynamic Conditional Correlation) model, the latter seen as the simplified and more scalable version of the former. These models were never used in wind speed studies before. Both models will be used with Gaussian innovations, and the VAR-DCC model will be also used with Student's t innovations. These models, which are able to dynamically couple volatilities, are compared to two benchmark model sets, the first set consisting of three independent univariate AR-GARCH (i.e. non-vector) models, the second set consisting of an individual VAR model without the GARCH sector. These benchmark models cannot dynamically couple volatilities. Both benchmark model sets will be used with Gaussian innovations. The time series used for the evaluation are taken from three North-American wind metering sites located at few tens of kilometers from each other. In order to highlight the usefulness of choosing autoregressive coupled volatility schemes for modeling and forecasting spatially and temporally varying wind speed data, a controlled variance electricity generation portfolio example of Markowitz type, typical of Energy Finance, is also discussed at some length. In all, it is concluded that the VAR-DCC model with Student's t innovations is the most balanced choice for this data set when the three goals of modeling, forecasting and scalability in the number of considered sites are taken into account at once. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We apply the CARDS solar forecasting tool, developed at the University of South Australia, to forecasting of solar radiation series at three sites in Guadeloupe in the Caribbean. After performing the model estimates at each individual site, forecast errors were tested for cross correlation. It was found that on an hourly time scale, there was small but significant correlation between sites, and this was taken into account in refining the forecast. Cross correlation was found to be insignificant at the ten minute time scale so this effect was not included in the forecasting. Also, the final error series in each case was tested for an ARCH effect, finding that to construct prediction intervals for the forecast a conditional heteroscedastic model had to be constructed for the variance. Note that cross correlation between sites has to be included for this procedure as well as in the forecasting of the radiation. [ABSTRACT FROM AUTHOR]

Abstract: To assess the viability of proposed solar installations, knowledge of global solar radiation is not sufficient. For stationary photovoltaic plant, we require global radiation series, but also the contemporaneous diffuse radiation series. Alternatively, for concentrated solar thermal, we need global and direct normal solar radiation. In this paper, we investigate whether one can simply use a model for predicting diffuse radiation using multiple predictions derived by our research team, the Boland–Ridley–Lauret (BRL) model, to give delineations of both diffuse and direct or if we need to use another model for direct or develop a new direct normal statistical model. [Copyright &y& Elsevier]

Abstract: This paper proposes to use a rather new statistical approach in the realm of solar radiation modelling namely Bayesian inference. In this work, the theory of Bayesian inference will be presented at length. The Bayesian analysis consists in two levels. The first one is related to the parameter estimation while the second one concerns the model selection problem. As an illustration, a Bayesian parameter estimation method is used to derive a logistic hourly solar diffuse fraction model. A major difference between Bayesian and frequentist (or classical) methods is that the Bayesian inference offers a framework (through the use of prior information) to continuously update our posterior beliefs. In other words, all previous work is not wasted as the preceding model’s parameters can be used as prior information for the derivation of the parameters estimates of the next (new) model. For this particular application, it is also shown that the use of Bayesian methods instead of classical statistical techniques lead to a less biased model. [Copyright &y& Elsevier]

Abstract: 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 . Boland et al. developed a validated model for Australian conditions. Boland et al. 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. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: The valuation of seagrass habitats, and indeed many natural resources, has been the focus of many researchers with differing approaches. This study presents an estimate of the value of seagrass habitats in terms of the contribution to secondary production of some important South Australian fish species in a broad scale domain. The approach we take considers the relationship between primary production estimates for seagrass habitats in temperate and subtropical coastal waters and estimates of commercial, recreational and discarded catch figures. The paper briefly describes models which link expected harvest of different species to seagrass area and the consequent expected reduction in catch given seagrass decline. We estimate that the economic contribution of seagrass habitats to secondary production in the gulf waters of South Australia is of the order of $ A114 million per year. Furthermore, we estimate in a particular example that the economic cost of a 16% decline of seagrass in one small area was $ 235,000 per year in lost production. [Copyright &y& Elsevier]

Abstract: We report the co-existence and growth of two different types of GdSi2 nanowires (NWs) following Gd deposition on the Si(100) surface. The first NW type is hexagonal and similar in appearance and orientation to that previously reported in the literature. The structure of the second NW type is unknown, but its orientation, thermodynamic stability and surface appearance are consistent with an orthorhombic structure. Real-time high-temperature STM studies show that these new NWs are thermodynamically preferred and in regions where both wires co-exist, they grow at the expense of their hexagonal counterparts. The implications of these observations for nanoscale interconnects is discussed. [Copyright &y& Elsevier]

The reaction of 1,3-cyclohexadiene (1,3-CHD) with Si dimers on the Si(100)-2×1 surface was studied via scanning tunnelling microscopy (STM) and density functional theory (DFT). Both intra- and interdimer [2+2] and [4+2] products are observed on the surface under empty-state bias conditions. Comparison of the observed products and their respective distribution on the surface with preliminary DFT calculations suggest a kinetically controlled reaction mechanism. [Copyright &y& Elsevier]

An innovative method is presented which simplifies the formal solution of the differential equations describing heat transfer in domestic dwellings. The method described here utilises Duhamel''s theorem and methods for solving Volterra integro-differential equations under certain conditions to develop the analytic solution to these differential equations. Previous work by the author on the level of complexity of weather data needed for simulation of a building''s thermal performance permits the use of this methodology. [Copyright &y& Elsevier]

Focuses on a study that showed that on an hour-to-hour basis the stochastic components of climate variables may be disregarded in building thermal, solar process heat and photovoltaic system performance without significantly affecting the result. Deterministic and stochastic components of the climatic variables; Construction of the weather data sets; Results.

Abstract In this paper a model is used to estimate the diffuse radiation using the Boland-Ridley-Lauret (BRL) model, which is developed for Chile, Costa Rica, and Australia. Additionally, artificial Direct Normal Irradiance time series are determined from the diffuse fraction results estimated by the model. Both estimates achieve excellent agreement based on the bias and normalized scatter. The relationship between the predictors of the model and the climatic conditions of each case of study, according to the Köppen-Geiger climatic classification was analyzed, which to the best of the authors' knowledge, it's the first study of this kind. An analysis on correlation and statistical significance was carried out between the model predictors and four determining geographic and climatic variables: altitude, latitude, precipitation and temperature. The statistical analysis shows that two of the six predictors correlate with temperature and precipitation and one predictor is correlated with latitude. Therefore, it can be suggested that the BRL model seems largely insensitive to the different regional climatic conditions, nevertheless, evaluation of the effect of the apparent correlation of the respective predictors and microclimatic and geographical variables points to further research in this area considering a wider selection of locations. Highlights • The BRL model predictors were calculated for Chile, Australia and Costa Rica. • Diffuse irradiance and DNI estimations were performed from GHI measurements. • The observed bias, as well as normalized scatter, were fairly low for hourly data. • BRL model predictors were correlated against the Köppen – Geiger climatic classification. • A non-negligible correlation was found between predictors and precipitation, temperature and latitude. [ABSTRACT FROM AUTHOR]

Emergent grain boundaries at free surface control material properties such as nanomaterial strength, catalysis, and corrosion. Recently the restructuring of emergent boundaries on copper (111) surfaces was discovered experimentally and atomic calculations point to its universality in fcc metal systems. Restructuring is due to a preference for boundaries to shift their tilt axis across the (1 1 ¯ 0) plane towards [112] and ultimately to form low energy [112] core shifted boundaries (CSBs). However, the observed geometry of these emergent boundaries is not reproduced by atomic calculations and the driving force is still controversial due to inconsistencies between the computational continuum analysis and atomic calculations. Here, using atomic calculations that involve a methodical shift of the dislocation core, we confirmed the geometry of emergent boundaries observed in experiment and reconciled the atomic calculations with the elastic analysis through the inclusion of a straight wedge disclination at the free surface. [Display omitted] [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Household sector (HS) is not only the major cause of waste generation in industrial sectors, but also the same function as an industrial sector to generate waste. Current researches mainly focus on waste generation caused by the demand of the HS based on the environmentally-extend input-output (EEIO) models while the effect of the HS as an industrial sector on waste flow has not been analysed. In addition, there is uncertainty around the economic cost of waste management discussed in EEIO models due to the lack of the calculation of the cost of labor. We adjust waste supply-use table to analyse effects of the HS as an industrial sector on waste flow, resulting in closed waste supply-use table (CWSUT). The novelty of the method lies in a shift in the effect of the HS, from an exogenous factor to an endogenous factor. Results derived from Australian CWSUT in 2009–10 illustrate waste generation effects of intermediate sectors and the mixed waste flows of the HS. The definition of “intermediate sector” is that the sector consumes intermediate inputs from producing sectors and generates intermediate outputs to Final demand. They show that: (1) the Construction sector has the largest waste generation effects, in which the amount of masonry waste has accounted for the most direct and total effects of waste generation; (2) investigations regarding the HS in CWSUT can calculate the amount of direct and total waste generation, the monetary flow, and effects of the Income for the Household sector. Base on the above results, the paper puts forwards the application of the CWSUT on other types of environmental issues and waste policies. [ABSTRACT FROM AUTHOR]

Building energy performance simulations are limited to typical meteorological weather conditions available in simulation software. Such simulations are insufficient for analysing energy performance sensitivity to a range of probable weather conditions. This research presents a method for developing robust meteorological weather data that can be used for energy performance sensitivity analysis without the need to access historical weather data. The method decomposes dry bulb temperature (DBT) and global horizontal solar radiation (H) into deterministic and stochastic components. For the typical weather data of the City of Adelaide, the deterministic component for each of DBT and H consists of a single frequency Fourier series. The stochastic components consist of 1-lag and 2-lags autoregressive models for DBT and H respectively. The stochastic components also include randomly selected values from the residuals of the autoregressive models. Based on this method, the coldest and hottest weather conditions were selected to simulate the energy performance of a single space. The results revealed 39% more cooling and 15% less heating in the hottest year, and 14% more heating and 64% less cooling in the coldest year. The results indicate that simulations based on typical weather conditions only are insufficient for assessing buildings' energy performance. [ABSTRACT FROM AUTHOR]