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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

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

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

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

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

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 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.

Concentrations of building mass and insufficient greenery in cities are identified to contribute significantly to extended heat stress in the built environment, commonly known as the urban heat island (UHI)effect. This paper presents a scenario-based modelling built on climate change projections and potential alterations of urban greenery in 2030 and 2090 in Adelaide, South Australia. The model is based on regional climate change scenarios, potential urban surface cover alternatives and resulted urban microclimate variations in Adelaide. Projected energy demand variations and corresponding carbon emission are calculated in each scenario. Results indicate that an ideal urban landscape transformation scenario with 30% tree canopy can effectively decrease the surface temperature by 1°C in winter and 3°C in summer by 2090. It is estimated that having greener and more heat resilient public spaces could save a total carbon emission of 140,000 tone CO2e in Adelaide annually compared to a business-as-usual scenario. Urban greenery may be used as a mean of increased urban life resilience to climate change by reducing surface temperature, increasing urban resilience and decreasing the energy demand during summer. 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

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 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

Waste input-output (WIO) model is a suitable method to explore the nexus between economic activities and waste management. Contemporary research that typically explores this nexus follows two main aspects: either they consider Final demand as a whole, or they identify the nexus between households,with different types of socio-demographic indicators and household waste generation. However, it is complex to apply the WIO model from the perspective of household consumption- a major component of Final demand d because of a lack of economic and environmental data related to household consumption.This paper proposes a new perspective, applying the WIO model to assess the nexus between different patterns of household consumption and indirect waste generation and treatment. This novelty is to combine macro- and micro-economic and environmental data related to Australian industrialsectors, different patterns of household consumption (Mosaic data), and direct waste generation into the WIO model for exploring this nexus in two scenarios. Results indicate that the total amount of indirectwaste generation caused by B05 (couples without children who spend the majority of their time at the office) are 99.24 kg more than that of D16 (couples without children who are retired and stay at home)for scenario I. The correlation coefficients for differences of output of economy and indirect waste generation between B05 and D16 are 0.9796 and 0.9773 in scenarios I and II, respectively. Sensitivity analysis indicates the change of the amount of direct waste generation in a reasonable range cannot dramatically affect the major economic activities and waste generation. This research suggests a different perspective of household consumption to estimate indirect waste generation through a WIO model to provide more reliable information for waste management in the supply chain. 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

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 using simulations based on typical weather conditions only are insufficient for assessing buildings' energy performance. 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

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

The process of finding a correct forecast equation for photovoltaic electricity production from renewable sources is an important matter, since knowing the factors affecting the increase in the proportion of renewable energy production and reducing the cost of the product has economic and scientific benefits. This paper proposes a mathematical model for forecasting energy production in photovoltaic (PV) panels based on a self-organizing feature map (SOFM) model. The proposed model is compared with other models, including the multi-layer perceptron (MLP) and support vector machine (SVM) models. Moreover, a mathematical model based on a polynomial function for fitting the desired output is proposed. Different practical measurement methods are used to validate the findings of the proposed neural and mathematical models such as mean square error (MSE), mean absolute error (MAE), correlation (R), and coefficient of determination (R2). The proposed SOFM model achieved a final MSE of 0.0007 in the training phase and 0.0005 in the cross-validation phase. In contrast, the SVM model resulted in a small MSE value equal to 0.0058, while the MLP model achieved a final MSE of 0.026 with a correlation coefficient of 0.9989, which indicates a strong relationship between input and output variables. The proposed SOFM model closely fits the desired results based on the R2 value, which is equal to 0.9555. Finally, the comparison results of MAE for the three models show that the SOFM model achieved a best result of 0.36156, whereas the SVM and MLP models yielded 4.53761 and 3.63927, respectively. A small MAE value indicates that the output of the SOFM model closely fits the actual results and predicts the desired output. 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

Separation models predict diffuse horizontal irradiance from other meteorological parameters such as the global horizontal irradiance or zenith angle. From a mathematical point of view, the separation modeling problem is a regression, where the regressors are observed or computed variables and the regressand is the unobserved diffuse fraction. The most successful minute-resolution separation model prior to 2016 was proposed by Engerer, which is constructed using a trend component (cloud enhancement) and a main effect (logistic function). Subsequently, the Starke model published in 2018 further improved the Engerer model. It is herein shown that the logistic-function type of model, and many other separation models, can be transformed into a (condition-based) multiple linear regression problem. Under this transformation framework, two new models are proposed, which strictly dominate the performance of the Engerer model and the Starke model, at all 7 test sites across the continental United States, making them probably the most accurate separation models to date. The new models are also tested at 5 European sites with unseen data (i.e., not involved during model parameter fitting); their performance again dominates all benchmarking models. The new separation models leverage half-hourly satellite-derived diffuse fraction. Since satellite data are available globally, the satellite-augmented separation models have universal applicability. However, despite their good performance, empirical separation models suffer from a series of issues. Hence, models driven by atmospheric physics are the “true gems” that one should pursue.Separation models predict diffuse horizontal irradiance from other meteorological parameters such as the global horizontal irradiance or zenith angle. From a mathematical point of view, the separation modeling problem is a regression, where the regressors are observed or computed variables and the regressand is the unobserved diffuse fraction. The most successful minute-resolution separation model prior to 2016 was proposed by Engerer, which is constructed using a trend component (cloud enhancement) and a main effect (logistic function). Subsequently, the Starke model published in 2018 further improved the Engerer model. It is herein shown that the logistic-function type of model, and many other separation models, can be transformed into a (condition-based) multiple linear regression problem. Under this transformation framework, two new models are proposed, which strictly dominate the performance of the Engerer model and the Starke model, at all 7 test sites across the continental United States, making th...

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. 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.

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

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.

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.

International audience; This work aims at assessing the performances of four models for their relative merits in estimating diffuse solar radiation on tilted planes for a southern location (Reunion Island). This comparative study benefits from a sound and consistent experimental set-up and is more detailed than the previous research works as 14 inclined planes are available for testing.

For the climatic variables solar irradiance and ambient temperature, a method has been devised for identifying the important cyclical components. After the contribution of the steady periodic part has been removed, the residual time series are analysed. It is shown that the daily solar irradiance residuals are a first-order autoregressive process while the daily average ambient temperature residuals are a third-order autoregressive process. This is independent of location. These results plus a knowledge of the characteristics of the cyclical components enable one to devise a methodology for generating synthetic series for these variables which are statistically indistinguishable from the original time series. Using this methodology, one can generate input data for models of heat flows in houses or solar hot water systems or other devices.

The trends of solar radiation are not easy to capture and become especially hard to predict when weather conditions change dramatically, such as with clouds blocking the sun. At present, the better performing methods to forecast solar radiation are time series methods, artificial neural networks and stochastic models. This paper will describe a new and efficient method capable of forecasting 1-h ahead solar radiation during cloudy days. The method combines an autoregressive (AR) model with a dynamical system model. In addition, the difference of solar radiation values at present and lag one time step is used as a correction to a predicted value, improving the forecasting accuracy by 30% compared to models without this correction. Refereed/Peer-reviewed

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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.

The determination of seasonal heating or cooling loads in domestic dwellings would be greatly simplified if the forcing functions of the dwellings, the climatic variables, could be represented using only the steady periodic or deterministic component. The differential equations in the time domain describing heat transfer processes could be transformed to algebraic equations in the frequency domain, which could be easily solved through matrix inversion. Temperatures or loads could then be found through transformation back to the time domain. It has been demonstrated that, for solar process heat systems, the stochastic or non-deterministic component of the ambient temperature can be ignored without greatly affecting energy determinations, although the stochastic component of the solar radiation has to be included to give realistic results. In this paper it is shown that the stochastic component is critical for both solar radiation and ambient temperature when determining heating and cooling loads for domestic dwellings.