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Grain boundaries with [111] tilt axes are common in polycrystalline face centered cubic metals. For copper (111) films, emergent grain boundaries close to surface have tilt axes that are shifted away from [111] that are lower in energy than the corresponding truncated bulk boundaries. Geometrical analysis and atomic calculations were used to study the driving force for this same relaxation phenomenon in representative fcc elemental metals. We show that the reduction in boundary energy scales with the elimination of energetically costly boundary core facets. We find that for a wide range of misorientation angles low energy core-shifted boundaries are also favored in Al, Ni, Au and Pt and discuss the significance for electromigration and other metal properties.

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 boundaries to shift their tilt axis across the (1-10) 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 axis in (1-10) 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 the boundary energies.

This project to identify the causes of Stringybark forest dieback we are witnessing and provide land managers with recommendations to enable them to maintain ecological function and to ensure that the forest cover of the Adelaide Hills can be sustained into the future. Refereed/Peer-reviewed

We prove a generalized form of the Granger-Johansen representation theorem (GJRT) for finite or infinite order integrated auto-regressive time series on Banach space, Comment: 29 pages, no figures, Preprint for article submitted to Journal of Time Series Econometrics

Two-dimensional layered semiconductors have recently emerged as attractive building blocks for next-generation low-power non-volatile memories. However, challenges remain in the controllable sub-micron fabrication of bipolar resistively switching circuit components from these novel materials. Here we report on the scalable experimental realisation of lateral on-dielectric memtransistors from monolayer single-crystal molybdenum disulfide (MoS2) utilising a focused helium ion beam. Site-specific irradiation with the probe of a helium ion microscope (HIM) allows for the creation of charged defects in the MoS2 lattice. The reversible drift of these locally seeded defects in the applied electric field modulates the resistance of the semiconducting channel, enabling versatile memristive functionality on the nanoscale. We find the device can reliably retain its resistance ratios and set biases for hundreds of switching cycles at sweep frequencies of up to 2.9 V/s with relatively low drain-source biases. We also demonstrate long-term potentiation and depression with sharp habituation that promises application in future neuromorphic architectures. This work advances the down-scaling progress of memristive devices without sacrificing key performance parameters such as power consumption or its applicability for synaptic emulation., Comment: 37 pages, 5 main figures, 14 supplementary figures

Precise tunability of electronic properties of 2D nanomaterials is a key goal of current research in this field of materials science. Chemical modification of layered transition metal dichalcogenides leads to the creation of heterostructures of low-dimensional variants of these materials. In particular, the effect of oxygen-containing plasma treatment on molybdenum disulfide (MoS$_2$) has long been thought to be detrimental to the electrical performance of the material. Here we show that the mobility and conductivity of MoS$_2$ can be precisely controlled and improved by systematic exposure to oxygen:argon plasma, and characterise the material utilising advanced spectroscopy and microscopy. Through complementary theoretical modelling which confirms conductivity enhancement, we uncover the role of a two-dimensional phase of molybdenum trioxide (2D-MoO$_3$) in improving the electronic behaviour of the material. Deduction of the beneficial role of MoO$_3$ will serve to open the field to new approaches with regard to the tunability of 2D semiconductors by their low-dimensional oxides in nano-modified heterostructures., Comment: 11 pages, 5 figures

The report was undertaken as part of a PhD research, funded by the CRC for LowCarbon Living Ltd. supported by the Cooperative Research Centres program, anAustralian Government initiative and a research student scholarship granted from theAustralian Building Codes Board.The results showed that a higher star rating does not necessarily coincide with a decrease in either cooling energy consumption, demand or overheating. The traditional uninsulated, double brick scenario required significantly more heating, however was able to outperform many high star rated homes during summer.Consequently, the integration of heat stress resistance in the Nationwide House Energy Rating Scheme would be a valued addition to the existing regulations to avoid building new homes with potentially lower coping capacity and increased dependence on air-conditioning. To address the problem, a new overheating analysis is proposed that can be implemented in the AccuRate.

This is a presentation given at the 2016 Planet Talks series at the Womadelaide World Music Festival.

Here we have investigated the influence of the antenna group position on both the formation of chiral amphiphilic EuIII-based self-assemblies in CH3CN solution and, on the ability to form monolayers on the surface of quartz substrates using the Langmuir–Blodgett technique, by changing from the 1-naphthyl (2(R), 2(S)) to the 2-naphthyl (1(R), 1(S)) position. The evaluation of binding constants of the self- assemblies in CH3CN solution was achieved using conventional techniques such as UV/Visible and luminescence spectroscopies along with more specific circular dichroism (CD) spectroscopy. The binding constants obtained for EuL, EuL2 and EuL3 species in the case of 2-naphthyl derivatives were comparable to those obtained for 1-naphthyl derivatives. The analysis of the changes in the CD spectra of 1(R) and 1(S) upon addition of EuIII not only allowed us to evaluate the values of the binding constants but the resulting recalculated spectra may also be used as fingerprints for assignment of the chiral self-assembly species formed in solution. The obtained monolayers were predominantly formed from EuL3 (≈85 %) with the minor species present in ≈15 % EuL2.

This is a presentation given at the 2016 Planet Talks series at the Womadelaide World Music Festival.

Distributed electrical energy storage can help reduce the CO2 emissions associated with the use of electrical energy, better enabling distributed generation of energy from sources such as rooftop photovoltaic (PV) systems. However, electricity distribution systems were not designed to allow power to flow from consumers to the grid, limiting how much power can be exported from rooftop PV systems. Falling feed-in tariffs are also making it more cost-effective to store excess PV energy on site, rather than export excess energy to the grid and then import it later at a higher cost.This study determines the impact and viability of distributed electrical energy storage systems for residential consumers with rooftop PV systems. It uses PV generation and the total power being drawn by electrical appliances for thirty-eight households at Adelaide’s Lochiel Park, a housing development of about 100 homes designed to demonstrate low energy and low water consumption housing. The study simulates energy storage and calculates the impact of this on the amount and cost of imported electricity. It shows energy storage can be used to reduce the overall cost of imported energy, and that decreasing feed-in tariffs and decreasing the cost of energy storage will lead to an uptake of energy storage systems over the next few years.

Appendix B. Analysis of the largest direct and indirect waste multipliers for 14 waste types. (PDF 129 kB)

Climate change is leading to an increased frequency and severity of heat waves. Spells of several consecutive days of unusually high temperatures have led to increased mortality rates for the more vulnerable in the community. The problem is compounded by the escalating energy costs and increasing peak electrical demand as people become more reliant on air conditioning. Domestic air conditioning is the primary determinant of peak power demand which has been a major driver of higher electricity costs. This report presents the findings of multidisciplinary research which develops a national framework to evaluate the potential impacts of heat waves. It presents a technical, social and economic approach to adapt Australian residential buildings to ameliorate the impact of heat waves in the community and reduce the risk of its adverse outcomes. Through the development of a methodology for estimating the impact of global warming on key weather parameters in 2030 and 2050, it is possible to re-evaluate the size and anticipated energy consumption of air conditioners in future years for various climate zones in Australia. Over the coming decades it is likely that mainland Australia will require more cooling than heating. While in some parts the total electricity usage for heating and cooling may remain unchanged, there is an overall significant increase in peak electricity demand, likely to further drive electricity prices. Through monitoring groups of households in South Australia, New South Wales and Queensland, the impact of heat waves on both thermal comfort sensation and energy consumption for air conditioning has been evaluated. The results show that households are likely to be able to tolerate slightly increased temperature levels indoors during periods of high outside temperatures. The research identified that household electricity costs are likely to rise above what is currently projected due to the impact of climate change. Through a number of regulatory changes to both household design and air conditioners, this impact can be minimised. A number of proposed retrofit and design measures are provided, which can readily reduce electricity usage for cooling at minimal cost to the household. Using a number of social research instruments, it is evident that households are willing to change behaviour rather than to spend money. Those on lower income and elderly individuals are the least able to afford the use of air conditioning and should be a priority for interventions and assistance. Increasing community awareness of cost effective strategies to manage comfort and health during heat waves is a high priority recommended action. Overall, the research showed that a combined approach including behaviour change, dwelling modification and improved air conditioner selection can readily adapt Australian households to the impact of heat waves, reducing the risk of heat related deaths and household energy costs.

International audience; L'exposé présente une méthode pour obtenir des estimateurs pour petites régions dans le contexte des Enquêtes sur le Niveau de Vie au Vietnam. On introduit brièvement ces enquêtes, puis on rappelle les concepts principaux en estimation pour petites régions, notamment l'utilisation de données auxiliaires, et on contraste les modèles simples avec ceux de régression. On traite les effets aléatoires dans ces modèles et on propose un modèle multi-niveaux pour une estimation au niveau de la commune au Vietnam, à notre connaissance le premier modèle de ce type construit à partir de données sur le niveau de vie au Vietnam. Notre modèle pour la moyenne au niveau communal du logarithme des dépenses familiales par personne utilise des variables indépendantes disponibles par le Recensement de 1999 et l'enquête aux ménages de 2002 et suit des idées exposées par Moura (1994, 1999); on discute comment mesurer la précision du modèle.

In this paper we discuss three rather special characteristics shared by many environmental problems. Namely, that (i) the environmental variables in which we are most interested constitute a stochastic process; (ii) the long-term or limiting behavior and the short-term or transient behavior are often both important; and (iii) the underlying probability distributions are likely to be influenced by the environmental policies or remedies that we choose to impose. This third characteristic implies the need to understand the impact of technologies and controls that influence the dynamics of the system. The control theoretic perspective of environmental engineering problems has, we believe, received less attention than it deserves in the literature. Consequently, after a brief discussion of the exogenous, control-independent case we focus on illustrating some special challenges and opportunities embedded in the control-dependent situations.

Hollow Manor is a programmatic work for chamber orchestra with electronic accompaniment. Specifically, it employs a flute, bass clarinet, Bb trumpet, and one of each, violin, viola, cello, conductor, and tape. This work has nine one-minute ambient settings, each creating the imagery of a room in an abandoned manor. The composer was inspired by video game scores to write Hollow Manor. His desire was to create original loop-based, and chamber music that could be used in a multimedia score. He chose to program and compose in the Reason music software. With video game scores as a model, this composition has rhythmically irregular electronic loops that are heard throughout. Section A, titled Corridor, greets the listener with the screeching and slamming of the front door. Section B, Parlor, employs an electronic choir and recalls an empty room where parties were once attended by aristocrats. Section C, Dining Room, is a scenario in which remnants of forgotten food remain on the serving table and flies are eating and laying their eggs. The melody of section D, Nursery, is created by phrases of children’s songs. The electronic accompaniment of section E, Game Room, recalls the entertainment of guests with music from a dilapidated phonograph. The music of section F, The Study, represents the stacks of books lining the walls.

Physical Review Letters

To obtain accurate information from a structural tool it is necessary to have an understanding of the physical principles which govern the interaction between the probe and the sample under investigation. In this thesis a detailed study of the physical basis for Extended X-ray Absorption Fine Structure (EXAFS) spectroscopy is presented. A single scattering formalism of EXAFS is introduced which allows a rigorous treatment of the central atom potential. A final state interaction formalism of EXAFS is also discussed. Multiple scattering processes are shown to be significant for systems of certain geometries. The standard single scattering EXAFS analysis produces erroneous results if the data contain a large multiple scattering contribution. The effect of thermal vibrations on such multiple scattering paths is also discussed. From symmetry considerations it is shown that only certain normal modes contribute to the Debye-Waller factor for a particular scattering path. Furthermore, changes in the scattering angles induced by thermal vibrations produces additional EXAFS components called modification factors. These factors are shown to be small for most systems. A study of the physical basis for the determination of structural information from EXAFS data is also presented. An objective method of determining the background absorption and the threshold energy is discussed and involves Gaussian functions. In addition, a scheme to determine the nature of the scattering atom in EXAFS experiments is introduced. This scheme is based on the fact that the phase intercept is a measure of the type of scattering atom. A method to determine bond distances is also discussed and does not require the use of model compounds or calculated phase shifts. The physical basis for this method is the absence of a linear term in the scattering phases. Therefore, it is possible to separate these phases from the linear term containing the distance information in the total phase.

Environmental context Nitrate (NO3−) and ammonium (NH4+) are the most important soil nitrogen forms for plant growth. However, conventional extraction techniques may introduce artefacts affecting the measurement of plant-available N concentrations following sampling and sample preparation processes. This is the first study of the DGT technique being used to measure NO3-N and NH4-N in a wide range of soils, compared with conventional KCl extraction, and examined different factors that contribute to the plant-availability of these ions in soils. The knowledge would help to optimise soil nitrogen management practices, increase economic benefits and reduce environmental impacts. Rationale The availability of soil nitrogen for plant uptake can be affected by numerous soil factors such as soil texture, moisture and organic matter content, temperature and microbial activity. Conventional extraction techniques may affect the measurement of plant-available N concentrations following sampling and sample preparation processes, including drying, sieving, homogenising, freezing and thawing. The diffusive gradients in thin-films (DGT) technique can overcome some limitations of the conventional extraction techniques and has been used to successfully estimate the plant-available fractions of nutrients, such as P, K, Zn, Cu and Mn in soils. Therefore, it is important to evaluate the use of DGT for measuring NO3− and NH4+ in a wide variety of soils and examine the factors that contribute to the plant-availability of these ions in soils. Methodology The experiment evaluated the ability of the DGT technique to measure NO3-N and NH4-N in soils using binding layers containing A520E anion exchange resin or Microlite® PrCH cation exchange resin, respectively. The DGT results were compared to those from conventional KCl extraction. Results The A520E- and PrCH-DGTs showed good detection limits for NO3-N (6.90 µg L−1) and NH4-N (6.23 µg L−1) and were able to measure potentially available NO3-N and NH4-N in unfertilised soils. The mass of NO3-N and NH4-N that accumulated on the DGT device increased linearly across soil concentrations ranging from 5 to 300 mg kg−1 NO3-N (depending on soil type) and 5–300 mg kg−1 NH4-N; which is equivalent to fertiliser rates of 75–450 kg ha−1 N. DGTs were used to measure potentially available NO3-N and NH4-N in ten soils with various physical and chemical properties. The DGT results were compared with conventional KCl extraction used to determine soil mineral N. DGT and KCl extraction measured values were significantly correlated with each other for NO3-N (R2 = 0.53; P-value

Refereed/Peer-reviewed Aim: The island species–area relationship (ISAR) assumes that the area of islands is homogeneous, or scales with environmental heterogeneity across an archipelago, which is not always the case. We compare the performance of models that adjust or substitute for island area with measures of habitat diversity, island age and resource availability for two taxonomic groups. Location: Five hotspot archipelagos (Azores, Galapagos, Hawaii, Cape Verde, Canary Islands). Taxa: Vascular plants, birds. Methods: We used the mathematical framework of the power law to compare relevant models, treating the one containing only area as a null model against which others were compared. Data were collated from databases and the literature. Models were compared using linear regression within archipelagos and via mixed effect models with archipelago as a random effect. Results: Weighting of island area by habitat diversity and resource availability systematically improved statistical significance and model fits versus the area only power law. Models including island age did not show the same systematic improvement in model fits. For vascular plants, weighting islands by resource availability (energy and water) performed better than weighting by habitat diversity, although for birds these weightings performed equally well. Main Conclusions: Given that islands within archipelagos are fairly heterogeneous in climate, topography and geology, it is worth accounting for this in ISARs. Our results suggest that, for islands in volcanic hotspot archipelagos, this is best done by using direct measures of habitat diversity and resource availability rather than using island age as a proxy. We, therefore, recommend using direct measures, rather than proxies, when investigating the drivers of biodiversity patterns on islands.

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

Soil erosion and sediment transport have significant consequences, including decreased agricultural production, water quality degradation, and modification to stream channels. Understanding these processes and their interactions with contributing factors is crucial for assessing the environmental impacts of erosion. The primary objective of this review is to identify a suitable soil erosion and sediment transport model for catchment-scale application. The study considers various model selection processes, including model capability and the spatial and temporal domains for assessing spatiotemporal distributions. The review acknowledges the limitations, uncertainties, and unrealistic assumptions associated with soil erosion and sediment transport models. Models are usually developed with a particular objective, which demands an assessment of capabilities, spatial, and temporal applicability, and catchment-scale applicability. Distributed models are often preferred for catchment-scale applications, as they can adequately account for spatial variations in erosion potential and sediment yield, aiding in the evaluation of erosion-contributing elements and planning erosion control measures. Based on the findings of this study, the authors encourage utilizing models (such as Soil and Water Assessment Tool (SWAT) or Automated Geospatial Watershed Assessment Tool (AGWA)) that can forecast net erosion as a function of sediment output for catchment erosion and sediment yield modeling. This review helps researchers and practitioners involved in erosion and sediment modeling by guiding the selection of an appropriate model type based on specific modeling purposes and basin scale. By choosing appropriate models, the accuracy and effectiveness of sediment yield estimation and erosion control measures can be improved. Refereed/Peer-reviewed

The species-area relationship (SAR) is widely applied in ecology. Mathematically, it is usually expressed as either a semi-log or power-law relationship, with the former being introduced by Gleason and the latter by Arrhenius. We here resolve the dispute about which form of the SAR to prefer by introducing a novel model that smoothly transforms between the Gleason semi-log (GSL) and Arrhenius power law (APL) forms. The model introduced has the form of lnq (S) = a + z ln A, with lnq being a generalized logarithmic function, which is a linear map (y = x) for q = 0 and a logarithmic map (y = ln x) for q = 1 and q can take any intermediate value between 0 and 1. We applied this model to 100 datasets (mostly islands), linking species richness to island area. The APL was the preferred model in 68% of head-to-head comparisons with the GSL. Both models were supported in 40% of cases. In just under half (44%) of the cases, an intermediate model best explained the data. The results demonstrate the utility of a simple intermediate SAR model. Visualizing the profile of the range of model fits for all q ∈ [0, 1] (a q chart) allows us to gain extra insight into SARs not yielded by head-to-head comparisons of GSL and APL. The mathematics related to the generalized logarithmic function introduced here should have applications to other areas of ecological modelling.

This study proposes a novel 10-point numerical scale to evaluate long-term thermal comfort satisfaction. The available scale that verbally evaluates the short-term thermal comfort is thermal sensation vote (TSV) and this has substituted for the long-term thermal comfort assessment. However, the TSV has been questioned by many studies due to the ambiguous, widely different interpretation of each score and unequal perceived gaps between the scores. The paper argues the biological similarities between pain and temperature perceptions and proposes a numerical thermal comfort scale like that of long-term pain perception. Participants in a survey rated their long-term summer thermal comfort in the natural ventilation mode through the numerical and TSV scales. Ratings were compared after converting to degree hours based on monitoring temperature data for the same summer. The results show that the numerical scale provides an opportunity for respondents to express their long-term thermal comfort satisfaction level. Scoring by the numerical scale was convenient and clear to the respondents. The verbal scores of TSV, had different interpretations for individuals as each point was attributed to more than two values of degree hours. The score ‘Neutral’ was significantly ambiguous for assessing thermal comfort. 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

A significant shift towards consolidating residential neighbourhoods has dramatically influenced the Australian national urban tree canopy benchmark. Recurrent tree planting, in densely settled residential suburbs, is an insufficient, emerging environmental and long-term energy conservation strategy. A global and Australian original research review, spanning the past two decades, reveals a better understanding of the link between trees and the built environment. This review defines potential tree allocation parameters, in urban energy conservation, within residential landscape constraints. This assessment focuses on regions similar to various Australian temperate to sub–tropical climate zones, defined as Mediterranean climate type by Köppen climate classification. Based on this review, the paper then identifies the importance of residential tree requirements and energy demand credibility projections as an amendment to existing metropolitan guidelines, using Sydney, Adelaide, and Perth in Australia as an example. Currently, Australian residential planning and design codes, and landscaping software action-codes, like iTree canopy software or Tree Planting Predictor Tool, do not consider residential tree arrangement. These parameters unify the building-energy assessment scheme with an optimal residential tree arrangement concept, leading to implementable residential development plans. This optimisation, primarily optimal residential tree arrangement, provides housing designers with ideal tree allocation data to yield the greatest effect. In addition, this optimal residential tree arrangement model will transform how researchers measure future urban canopy cover performance. Refereed/Peer-reviewed

The conveyance of stormwater has become a major concern for urban planners, considering its harmful effects for receiving water bodies, potentially disturbing their ecosystem. Therefore, it is important to characterize the quality of catchment outflows. This information can assist in planning for appropriate mitigation measures to reduce stormwater runoff discharge from the catchment. To achieve this aim, the article reports the field data from a typical urban catchment in Australia. The pollutant concentration from laboratory testing is then compared against national and international reported values. In addition, a stochastic catchment model was prepared using MUSIC. The study in particular reported on the techniques to model distributed curbside leaky wells with appropriate level of aggregation. The model informed regarding the efficacy of distributed curbside leaky well systems to improve the stormwater quality. The results indicated that catchment generated pollutant load, which is typical of Australian residential catchments. The use of distributed storages only marginally improves the quality of catchment outflows. It is because ability of distributed leaky wells depended on the intercepted runoff volume which is dependent on the hydrological storage volume of each device. Therefore, limited storage volume of current systems resulted in higher contributing area to storage ratio. This manifested in marginal intercepted volume, thereby only minimum reduction in pollutant transport from the catchment to outlet. Considering strong correlation between contributing impervious area and runoff pollutant generation, the study raised the concern that in lieu of following the policy of infill development, there can be potential increase in pollutant concentration in runoff outflows from Australian residential catchments. It is recommended to monitor stormwater quality from more residential catchments in their present conditions. This will assist in informed decision-making regarding adopting mitigations measures before considering developments. Refereed/Peer-reviewed

Refereed/Peer-reviewed Precise solar radiation forecasting can provide great benefits and solutions for smart grid distribution and electricity management. However, its non-stationary behavior and randomness render its estimation very difficult. In this respect, a new hybrid learning approach is proposed for multi-hour global solar radiation forecasting, relying on Convolutional Neural Network (CNN), Nonparametric Gaussian Process Regression (GPR), Least Support Vector Machine (LS-SVM), and Extreme Learning Machine (ELM) as essence predictors. Then compressive sensing technique is applied to perform a hybridization scheme of the model’s output. Hourly global solar radiation data from two sites in Algeria with different climate conditions are used to evaluate the full potential of the integrated model, with stationarity checks with an advanced clear sky model (MecClear model). Different comparative simulations show the superiority of the proposed pipeline in forecasting hourly global solar radiation data for multi-hour ahead compared to the stand-alone model. Experimental results show that the proposed hybridization methodology can effectively improve the prediction accuracy and outperforms benchmarking models during all the forecasting horizons.

Batteries are undoubtedly part of our energy future. Should you put one in your home now to store solar output, manage your energy use and cut costs? It really depends on what you want to achieve.

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

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. Refereed/Peer-reviewed

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. Refereed/Peer-reviewed

A Photovoltaic/Thermal (PV/T) system combines PV and thermal collector, which is considered promising technology especially for building integrated PV/T system. The PV/T cooling systems using water, water-PCM and nanofluid/nano-PCM moves through the cooling pipes were investigated, in this study. However, this paper focuses on testing different PV/T systems (conventional PV, water-based PVT, water-nanofluid PVT, and nanofluid/nano-PCM) under the same conditions and environment using one artificial neural network (ANN) based Multi-Layer Perceptron (MLP) system. Also, investigate the differences in the efficiency of these systems on both thermal and electrical when using only one simulation system (MLP). The proposed ANN approach proved that using of nanofluid/nano-PCM was enhanced the electrical efficiency from 8.07% to 13.32% and its thermal efficiency reached 72%. Also, the voltage was improved significantly. Many measurement methods were used for validating the results of the proposed ANN model like the Mean Absolute Error (MAE), Mean Square Error (MSE), Correlation (R), and coefficient of determination (R 2 ). The proposed ANN model achieved a final MSE of 0.0229 in the training phase and 0.0282 in the cross-validation phase. The sensitivity analysis showed that the influence of solar irradiation and Amb-temp almost has a constant effect on electrical efficiency. However, the Ambient temperature had a significant impact on thermal efficiency. The results of the network were consistent with the experimental results of the current study and published works. 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

We develop a new probabilistic forecasting method for global horizontal irradiation (GHI) by extending our previous bootstrap method to a case of an exponentially decaying heteroscedastic model for tracking dynamics in solar radiance. Our previous method catered for the global systematic variation in variance of solar radiation, whereas our new method also caters for the local variation in variance. We test the performance of our new probabilistic forecasting method against our old probabilistic forecasting method at three locations: Adelaide, Darwin, and Mildura. These locations are chosen to represent three distinct climates. The prediction interval coverage probability, prediction interval normalized averaged width and Winkler score results from our new probabilistic forecasting method are encouraging. Our new method performs better than our previous method at Adelaide and Mildura, regions with a higher proportion of clear-sky days, whereas our previous method performs better than our new method at Darwin, a region with a lower proportion of clear-sky days. These results suggest that the ideal probabilistic forecasting method might be climate specific.

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

Intermittent electrical power output from grid-connected solar farms causes intermittent and uncertain requirements for dispatchable power to balance power supply and demand. Accurate forecasting of electrical power output from solar farms can improve managing power generators connected to the grid. To forecast the electrical power output, a time series model is developed for two solar farms in Australia. The forecast model consists of a Fourier series that models seasonality and an autoregressive moving-average component that models the difference between the observed electrical power outputs and the Fourier series. Persistence detection is added to the model to improve forecast performance on clear days. Using minutely data, the model forecasts the electrical power output seven minutes ahead at every five-minute interval to comply with the requirements of the Australian Energy Market Operator (AEMO). Based on a 30-day testing period, the normalized mean absolute error (NMAE) skills of the time series model are 10.9% and 13.2% lower than those of the clear sky index persistence (CSIP)model. However, the normalized root mean squared error (NRMSE) skills of the time series model are approximately 3% and 12% higher than those of CSIP and the model currently used by AEMO, respectively. As the NRMSE skills are more indicative than the NMAE skills in reducing large forecast errors that would reduce electricity grid stability, the results suggest that AEMO can improve the management of the electricity grid with an inexpensive tool by adopting the developed model to forecast electrical power output of solar farms. 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

Despite the variety of irradiance separation models available in the literature, there is not yet a model that performs equally well for every location worldwide. Nevertheless, separation models still represent an interesting approach when only global irradiance is measured. This work presents further developments of the recently proposed Boland-Ridley-Lauret minute model and proposes 1-min diffuse fraction models for each climate zone in the globe. To accomplish this, 1-min worldwide irradiance data from 51 the Baseline Surface Rresearch Network stations and one from Australian Bureau of Meteorology were used. The Köppen-Geiger classification was used to determine the climate zone of each station: both the simple climate classification (A, B, C, D, E) and the complete classification (Aw, Bsh, etc.). Furthermore, a robust nonlinear regression method was used to build the separation models without removing measurement outliers in advance. The climate-specific models proposed herein present better performance than other models from the literature, such as the BRL, Perez and Skartveit models (developed using hourly irradiance data) and the Engerer model (developed using minute data). Generally, the models proposed for climate zones in the complete Köppen classification presented better similitude with the measured data than the models for the simple classification, especially for stations within climate zones B and E. Refereed/Peer-reviewed

Solar energy is an economic and clean power source subject to natural variability, while energy storage might attenuate it, ultimately, effective and operationally feasible forecasting techniques for energy management are needed for better grid integration. This work presents a novel deterministic forecast method considering: irradiance pattern classification, Markov chains, fuzzy logic and an operational approach. The method developed was applied in a rolling manner for six years to a target location with no prior data to assess performance and its changes as new local data becomes available. Clearness index, diffuse fraction and irradiance hourly forecasts are analyzed on a yearly basis but also for 20 day types, and compared against smart persistence. Results show the proposed method outperforms smart persistence by ~10% for clearness index and diffuse fraction on the base case, but there are significant differences across the 20 day types analyzed, reaching up to +60% for clear days. Forecast lead time has the greatest impact in forecasting performance, which is important for any practical implementation. Seasonality in data gaps or rejected data can have a definite effect in performance assessment. A novel, comprehensive and detailed analysis framework was shown to present a better assessment of forecasters’ performance Refereed/Peer-reviewed

The Mathematics Clinic at the University of South Australia runs a year-long sponsored research project for teams of students in their final year of study in the Bachelor of Mathematics. In 2020 the Department for Energy and Mining partnered with the university, providing the Olympic Domain mineral deposits project. The main aim of the project was to analyse multiple types of geoscientific data to examine relationships between them and mineral deposit locations within the Olympic Domain across a study area of 100 x 100 km near Olympic Dam (Fig 1). Scientific and quantitative method was used to analyse physical properties of the cover and basement rocks as well as the predictive potential of these data to narrow the search space for mineral deposits, in particular copper occurrences.

In this chapter we first describe the basic concepts of synthetic generation of time series data. We examine several of the realms where it is needed. One area is downscaling from a coarse temporal resolution to a higher frequency. For example, when estimating the performance of solar cells, one-minute data is more useful than hourly, but it may not be available. Also, infilling missing data is crucial if one is forecasting output from a solar farm. On the other hand, generating any number of years of data from a limited number of measured years—known as bootstrapping—is useful for estimating risk for farms and other solar installations. In energy markets, there is scope for generating possible future trajectories for either solar radiation time series or financial instruments that depend on them. In describing these situations and others, we briefly explain the mechanisms for these computations. We end by describing in detail some specific example applications of synthetic solar irradiance tools.

The field of energy forecasting has attracted many researchers from different fields (e.g., meteorology, data sciences, mechanical or electrical engineering) over the last decade. Solar forecasting is a fast-growing subdomain of energy forecasting. Despite several previous attempts, the methods and measures used for verification of deterministic (also known as single-valued or point) solar forecasts are still far from being standardized, making forecast analysis and comparison difficult. To analyze and compare solar forecasts, the well-established Murphy–Winkler framework for distribution-oriented forecast verification is recommended as a standard practice. This framework examines aspects of forecast quality, such as reliability, resolution, association, or discrimination, and analyzes the joint distribution of forecasts and observations, which contains all time-independent information relevant to verification. To verify forecasts, one can use any graphical display or mathematical/statistical measure to provide insights and summarize the aspects of forecast quality. The majority of graphical methods and accuracy measures known to solar forecasters are specific methods under this general framework. Additionally, measuring the overall skillfulness of forecasters is also of general interest. The use of the root mean square error (RMSE) skill score based on the optimal convex combination of climatology and persistence methods is highly recommended. By standardizing the accuracy measure and reference forecasting method, the RMSE skill score allows—with appropriate caveats—comparison of forecasts made using different models, across different locations and time periods. Refereed/Peer-reviewed

This article proposes a new hybrid least squares-support vector machine and artificial bee colony algorithm (ABC-LS-SVM) for multi-hour ahead forecasting of global solar radiation (GHI) data. The framework performs on training the least squares-support vector machine (LS-SVM) model by means of the ABC algorithm using the measured data. ABC is developed for free parameters optimization for the LS-SVM model in a search space so as to boost the forecasting performance. The developed ABC-LS-SVM approach is verified on an hourly scale on a database of five years of measurements. The measured data were collected from 2013 to 2017 at the Applied Research Unit for Renewable Energy (URAER) in Ghardaia, south of Algeria. Several combinations of input data have been tested to model the desired output. Forecasting results of 12 h ahead GHI with the ABC-LS-SVM model led to the root-mean-square error (RMSE) equal to 116.22 Wh/m2, Correlation coefficient r = 94.3%. With the classical LS-SVM, the RMSE error equals to 117.73 Wh/m2 and correlation coefficient r = 92.42%; for cuckoo search algorithm combined with LS-SVM, the RMSE = 116.89 Wh/m2 and r = 93.78%. The results achieved reveal that the proposed hybridization scheme provides a more accurate performance compared to cuckoo search-LS-SVM and the stand-alone LS-SVM.