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With the fast increase of solar energy plants, a high-quality short-term forecast is required to smoothly integrate their production in the electricity grids. Usually, forecasting systems predict the future solar energy as a continuous variable. But for particular applications, such as concentrated solar plants with tracking devices, the operator needs to anticipate the achievement of a solar irradiance threshold to start or stop their system. In this case, binary forecasts are more relevant. Moreover, while most forecasting systems are deterministic, the probabilistic approach provides additional information about their inherent uncertainty that is essential for decision-making. The objective of this work is to propose a methodology to generate probabilistic solar forecasts as a binary event for very short-term horizons between 1 and 30 min. Among the various techniques developed to predict the solar potential for the next few minutes, sky imagery is one of the most promising. Therefore, we propose in this work to combine a state-of-the-art model based on a sky camera and a discrete choice model to predict the probability of an irradiance threshold suitable for plant operators. Two well-known parametric discrete choice models, logit and probit models, and a machine learning technique, random forest, were tested to post-process the deterministic forecast derived from sky images. All three models significantly improve the quality of the original deterministic forecast. However, random forest gives the best results and especially provides reliable probability predictions. [ABSTRACT FROM AUTHOR]

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

• A new reference model for probabilistic solar forecasts called "CSD-CLIM" is introduced. • There is no need to form probability distributions to compute its CRPS. • A baseline for climatology benchmark models is proposed and used to compare models. • CSD-CLIM is easy-to-implement and meets all prerequisite properties of a good benchmark model. • CSD-CLIM achieves the best trade-off between reliability and resolution. • It can be a viable alternative to existing reference models. Probabilistic solar forecasting is becoming a major topic in the solar research community as it provides more information about the uncertainty of the forecast compared to deterministic forecasting. However, to facilitate the adoption of probabilistic forecasts within solar forecasting communities (industry and academic), the definition and the use of standardized best practices are a prerequisite. Among others, there is a need for benchmark models that are able to properly assess the performance of new probabilistic forecasting methods. In this work, we propose a new climatology benchmark model called "CSD-CLIM" (for Clear-Sky Dependent Climatology). This new reference model is evaluated against two other climatology benchmark models namely the naive climatology and a well-referenced model in the literature, the CH-PeEn (for Complete History Persistence Ensemble). The verification of compliance with a set of properties that a climatology benchmark model must follow demonstrates that the new CSD-CLIM model outperforms the naive climatology and that it can be a viable alternative to the CH-PeEn model. It is shown that the better performance of CSD-CLIM is due to a specific binning of the historical irradiance data based on the clear-sky irradiance values. [ABSTRACT FROM AUTHOR]

• A framework for evaluating solar irradiance probabilistic forecasts is proposed. • The verification framework is based on visual diagnostic tools and a set of scores. • The verification metrics are applied to ensemble and quantile forecasts. • It is recommended to use a set of scores to assess the quality of the forecasts. We propose a framework for evaluating the quality of solar irradiance probabilistic forecasts. The verification framework is based on visual diagnostic tools and a set of scoring rules mostly originating from the weather forecast verification community. Two types of probabilistic forecasts are used as a basis to illustrate the application of these verification approaches. The first one consists in ensemble forecasts commonly provided by national or international meteorological centres. The second one originates from statistical methods and produces a set of discrete quantile forecasts, the nominal proportions of which span the unit interval. These probabilistic forecasts are evaluated for two selected sites that experience very different climatic conditions. The first site is located in the continental US while the second one is situated on La Réunion Island. Although visual diagnostic tools can help identify deficiencies in generated forecasts, it is recommended that a set of numerical scores be used to assess the quality of probabilistic forecasts. In particular, the Continuous Ranked Probability Score (CRPS) seems to have all the features needed to evaluate a probabilistic forecasting system and, as such, may become a standard for verifying solar irradiance probabilistic forecasts and by extension probabilistic forecasts of solar power generation. [ABSTRACT FROM AUTHOR]

In this work, we assess the performance of three probabilistic models for intra-day solar forecasting. More precisely, a linear quantile regression method is used to build three models for generating 1 h-6 h-ahead probabilistic forecasts. Our approach is applied to forecasting solar irradiance at a site experiencing highly variable sky conditions using the historical ground observations of solar irradiance as endogenous inputs and day-ahead forecasts as exogenous inputs. Day-ahead irradiance forecasts are obtained from the Integrated Forecast System (IFS), a Numerical Weather Prediction (NWP) model maintained by the European Center for Medium-RangeWeather Forecast (ECMWF). Several metrics, mainly originated from the weather forecasting community, are used to evaluate the performance of the probabilistic forecasts. The results demonstrated that the NWP exogenous inputs improve the quality of the intra-day probabilistic forecasts. The analysis considered two locations with very dissimilar solar variability. Comparison between the two locations highlighted that the statistical performance of the probabilistic models depends on the local sky conditions. [ABSTRACT FROM AUTHOR]

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

The presence and abundance of pathogen inoculum is with host resistance and environmental conditions a key factor in epidemic development. Therefore, several spore-sampling devices have been proposed to monitor pathogen inoculum above fields. However, to make spore sampling more reliable as a management tool and to facilitate its adoption, information on infection efficiency and molecular tools for estimating airborne sporangia concentration are needed. Experiments were thus undertaken in a growth chamber to study the infection efficiency of four clonal lineages of P. infestans (US-8, US-11, US-23, and US-24) by measuring the airborne sporangia concentration and resulting disease intensity. The relationship between the airborne sporangia concentration and the number of lesions per leaf was exponential. For the same concentration, the sporangia of US-23 caused significantly more lesions than the sporangia of the other clonal lineages did. Under optimal conditions, an airborne sporangia concentration of 10 sporangia m−3 for US-23 was sufficient to cause one lesion per leaf, whereas for the other clonal lineages, it took 15 to 25 sporangia m−3 to reach the same disease intensity. However, in terms of diseased leaf area, there was no difference between clonal lineages US-8, US-23 and US-24. Also, a sensitive quantitative real-time polymerase chain reaction (qPCR) tool was developed to quantify P. infestans airborne sporangia with detection sensitivity of one sporangium. The specificity of the qPCR assay was rigorously tested for airborne inoculum and was either similar to, or an improvement on, other published PCR assays. This assay allows rapid and reliable detection and quantification of P. infestans airborne sporangia and thereby, facilitates the implementation of spores-sampling network. [ABSTRACT FROM AUTHOR]

Highlights: [•] WRF model is used to forecast hourly global solar radiation at Reunion Island. [•] Kalman filtering is applied to enhance WRF forecasts. [•] Hourly post-processing of GHI forecasted with Kalman filtering gave better results than direct WRF output. [•] rRMSE decreased from 37.2% to 22.24% and rMBE from 6.34% to 0.3%. [Copyright &y& Elsevier]

Abstract: In this paper, two ways of increasing the integration of wind and solar energy into the electricity grid through energy storage are analyzed. The first service (S1) to the electricity grid is related to a smoothed and hourly scheduled daily production while the second one (S2) concerns a constant and guaranteed minimal production. A power bid, based on meteorological forecasts, is transmitted a day ahead by the producer to the utility grid operator. This leads to a yearly default time rate for which the actual power supplied does not meet the announcement within a given tolerance. The modelling approach developed in this study enables to infer the optimal operation of the system and more specifically the optimal size of the energy storage, aiming at reducing the default time rate (DTR) under 5%. The simulations consider PV or wind with storage systems having discharge time in the range of minutes. Two real test cases are examined: Guadeloupe Island for wind and Reunion Island for PV. The results show that both of the two services can be achieved under specific conditions and that an optimal day-ahead power bid with a 2% DTR is possible with a storage capacity of 1 MWh per installed MWp. In addition, a linear strategy of forecasting this optimal power is highly correlated to the precision of upstream meteorological forecast. [Copyright &y& Elsevier]

Abstract: 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 numerical 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 insular grids. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: For the last two decades, economic development in Reunion Island has led to major structural changes. The latter have been characterized by an increase in energy demand per person. This demand is mostly related to a high population growth (1.55% per year). Reunion currently has 833,000 inhabitants. The population will rise to 1 million in 2030. Like most ultraperipheral regions of the European Union, Reunion is heavily dependent on imported fossil fuels for its energy production. The total primary energy consumption amounted to 1352ktep in 2009 and 87.1% is imported energy. The development of various renewable energies such as solar energy, biomass, ocean energy, etc. is thus of priority concern to aim to achieve energetic independence. Just like other French overseas territories, Reunion policies have widely invested in Renewable Energy Sources (RES) since 2000. This paper aims at presenting the current status, the major achievements of policies and the future objectives in the deployment of renewable energy programmes. The perspective of a net zero energy island versus the pressure of the population is analysed. The barriers to penetration of RES in a small-scale territory are also discussed. [Copyright &y& Elsevier]

The article offers information on the plant pathology of onion and management of the Botrytis leaf blight (BLB) disease caused by Botrytis squamosa. It explores the history of the disease and the intensive research on its pathogen development and epidemiology on varied onion cultivars. It notes that BLB management of sclerorial populations and fungicide-insensitive isolates must initiate fungicide sprays at or before disease onset to maintain a low level of disease during bulb formation.

This paper focuses on the modelling of occupant behaviour in the case of a non-residential mixed-mode building on the tropical island of La Réunion. For such areas and types of buildings, occupants can operate passive solutions to achieve comfort while energy-consuming ones can offer alternatives during the hottest months. Yet, compared to other climatic zones, specific knowledge on occupant comfort and behaviour is limited, making the work of engineers difficult during the design phase. In this work, occupants' operations on hygrothermal comfort controls, such as windows and fans, were first measured and analysed. Secondly, these behaviours were modelled using two deterministic methods based on machine learning techniques and a probabilistic graphical model. A model was also implemented to estimate the number of people, using the power demand of the electrical outlets. The estimation ability of the behavioural models was evaluated and led to F1 scores greater than 0.7. A two-classifier model was proposed to estimate the level of ceiling fan use. This combined model slightly improves the F1 scores by more that 2%, which demonstrates the necessity of taking into account the links between the different controls. [ABSTRACT FROM AUTHOR]

Accurate solar forecasts is one of the most effective solution to enhance grid operations. As the solar resource is intrinsically uncertain, a growing interest for solar probabilistic forecasts is observed in the solar research community. In this work, we compare two approaches for the generation of day-ahead solar irradiance probabilistic forecasts. The first class of models termed as deterministic-based models generates probabilistic forecasts from a deterministic value of the irradiance predicted by a Numerical Weather Prediction (NWP) model. The second type of models denoted by ensemble-based models issues probabilistic forecasts through the calibration of an Ensemble Prediction System (EPS) or from information (such as mean and variance) derived from the ensemble. The verification of the probabilistic forecasts is made using a sound framework. A numerical score, the Continuous Ranked Probability Score (CRPS), is used to assess the overall performance of the different models. The decomposition of the CRPS into reliability and resolution provides a further detailed insight into the quality of the probabilistic forecasts. In addition, a new diagnostic tool which evaluates the contribution of the statistical moments of the forecast distributions to the CRPS is proposed. This tool denoted by MC-CRPS allows identifying the characteristics of an ensemble that have an impact on the quality of the probabilistic forecasts. The assessment of the different models is done on several sites experiencing very different climatic conditions. Results show a general superior performance of ensemble-based models as the gain in forecast quality measured by the CRPS ranges from 4% to 16% depending on the site. • Probabilistic forecasts from deterministic informations and from Ensembles compared. • Several models either parametric and non-parametric are compared. • New tool to quantify the contributions of the statistical moments to the quality. • Sites with very different sky conditions are considered. • Using Ensemble Prediction Systems improve the quality of forecasts. [ABSTRACT FROM AUTHOR]

This work compares the performance of machine learning methods ( k -nearest-neighbors ( k NN) and gradient boosting (GB)) in intra-hour forecasting of global (GHI) and direct normal (DNI) irradiances. The models predict the GHI and DNI and the corresponding prediction intervals. The data used in this work include pyranometer measurements of GHI and DNI and sky images. Point forecasts are evaluated using bulk error metrics while the performance of the probabilistic forecasts are quantified using metrics such as Prediction Interval Coverage Probability (PICP), Prediction Interval Normalized Averaged Width (PINAW) and the Continuous Ranked Probability Score (CRPS). Graphical verification displays like reliability diagram and rank histogram are used to assess the probabilistic forecasts. Results show that the machine learning models achieve significant forecast improvements over the reference model. The reduction in the RMSE translates into forecasting skills ranging between 8% and 24%, and 10% and 30% for the GHI and DNI testing set, respectively. CRPS skill scores of 42% and 62% are obtained respectively for GHI and DNI probabilistic forecasts. Regarding the point forecasts, the GB method performs better than the k NN method when sky image features are included in the model. Conversely, for probabilistic forecasts the k NN exhibits rather good performance. [ABSTRACT FROM AUTHOR]

Real-time loop-mediated isothermal amplification (LAMP) and recombinase polymerase amplification (RPA) assays were developed targeting the internal transcribed spacer 2 region of the ribosomal DNA of Phytophthora infestans, the potato late blight causal agent. A rapid crude plant extract (CPE) preparation method from infected potato leaves was developed for on-site testing. The assay's specificity was tested using several species of Phytophthora and other potato fungal and oomycete pathogens. Both LAMP and RPA assays showed specificity to P. infestans but also to the closely related species P. andina, P. mirabilis, P. phaseoli, and P. ipomoeae, although the latter are not reported as potato pathogen species. No cross-reaction occurred with P. capsici or with the potato pathogens tested, including P. nicotianae and P. erythroseptica. The sensitivity was determined using P. infestans pure genomic DNA added into healthy CPE samples. Both LAMP and RPA assays detected DNA at 50 fg/µl and were insensitive to CPE inhibition. The isothermal assays were tested with artificially inoculated and naturally infected potato plants using a Smart-DART platform. The LAMP assay effectively detected P. infestans in symptomless potato leaves as soon as 24 h postinoculation. A rapid and accurate on-site detection of P. infestans in plant material using the LAMP assay will contribute to improved late blight diagnosis and early detection of infections and facilitate prompt management decisions. [ABSTRACT FROM AUTHOR]

In this paper, emphasis is put on the design of a neural network (NN) to model the direct solar irradiance. Since, unfortunately, a neural network is not a statistician-in-a-box, building a NN for a particular problem is a nontrivial task. As a consequence, we argue that in order to properly model the direct solar irradiance, a systematic methodology must be employed. For this purpose, we propose a two-step approach to building the NN model. The first step deals with a probabilistic interpretation of the NN learning by using Bayesian techniques. The Bayesian approach to modeling offers significant advantages over the classical NN learning process. Among others, one can cite (i) automatic complexity control of the NN using all the available data and (ii) selection of the most important input variables. The second step consists of using a new sensitivity analysis-based pruning method in order to infer the optimal NN structure. We show that the combination of the two approaches makes the practical implementation of the Bayesian techniques more reliable. [ABSTRACT FROM AUTHOR]

In this paper a techno-economic comparison of an energy storage system (ESS) sizing for three intermittent renewables, wind, wave and PV power, with regard to two electricity grid services is presented. The first service consists of output hourly smoothing, based on day-ahead power forecasts (S1). The second service supplies year-round guaranteed power (S2). This leads to an annual default time rate (DTR) for which the actual power supplied to the grid does not match the day-ahead power bid within a given tolerance. A heuristic optimization based on an Adaptive Storage Operation (ASO) scheduling is developed in this study. ASO enables the minimal 5%-DTR ESS capacity, power, energy and feed-in-tariffs to be inferred from the operating conditions, depending on tolerance. The simulations assess and compare the techno-economic viability and efficiency of every renewable sources coupled with ESS. PV power is more efficient with daylight hours restricted services and higher power levels can be guaranteed for S1. Wind and wave power are more suitable than PV for services dedicated to full-day power delivery, as in the case of S2. For hourly smoothing the forecast accuracy influence is studied and yields a high impact on techno-economic sizing. [ABSTRACT FROM AUTHOR]

The ‘Foot-in-the-door’ (FITD) is a well-known compliance technique that increases compliance with a request. Many investigations on this paradigm have generally used prosocial requests to test the effect of the technique. A new evaluation of the effect of the FITD technique was carried out on tobacco deprivation. A two feet-in-the-door technique in which the target request was preceded by two small target requests was used to encourage students to stop smoking for 24 h. The results were compared with two single foot-in-the- door procedures in which the final request was only preceded by one small request, as well as with a control condition using only a 24 h stop-smoking request. Results showed that the single FITD and the two feet-in-the-door procedures were effective to increase verbal compliance (accepting to stop smoking) but only the two feet-in-the-door technique significantly increased behavioural compliance (not smoking for 24 h) with the request. [ABSTRACT FROM PUBLISHER]

Many health campaigns are designed to reduce dangerous binge drinking by challenging the drinking perceived norm. Both information about health risks and statements that only a few people binge drink (descriptive norm) threaten self-integrity for individuals targeted. So, to combat this self-threat and preserve their positive self-integrity, drinkers discredit the message as a coping strategy. An alternative to the coping strategy is a procedure of self-affirmation to protect self-integrity. Across three experiments, we found that self-affirmation does indeed reduce (Experiment 1) or delete (Experiments 2 and 3) discrediting, but only provided that there is no normative information in the health message. Individuals continued to use the discrediting strategy despite the fact that the participants were self-affirmed when they are told that few people binge drink among their age group. The theoretical implications for self-affirmation are discussed. [ABSTRACT FROM PUBLISHER]

This paper aims at assessing the accuracy of different solar forecasting methods in the case of an insular context. Two sites of La Réunion Island, Le Tampon and Saint-Pierre, are chosen to do the benchmarking exercise. Réunion Island is a tropical island with a complex orography where cloud processes are mainly governed by local dynamics. As a consequence, Réunion Island exhibits numerous micro-climates. The two aforementioned sites are quite representative of the challenging character of solar forecasting in the case of a tropical island with complex orography. Hence, although distant from only 10 km, these two sites exhibit very different sky conditions. This work focuses on day-ahead and intra-day solar forecasting. Day-ahead solar forecasts are provided by the European Center for Medium-Range Weather Forecast (ECMWF). This organization maintains and runs the Numerical Weather Prediction (NWP) model named Integrated Forecast System (IFS). In this work, post-processing techniques are applied to refine the output of the IFS model for day-ahead forecasting. Statistical models like a recursive linear model or a nonlinear model such as an artificial neural network are used to produce the intra-day solar forecasts. It is shown that a combination of the IFS model and the neural network model further improves the accuracy of the forecasts. [ABSTRACT FROM AUTHOR]

Despite the growing awareness in academia and industry of the importance of solar probabilistic forecasting for further enhancing the integration of variable photovoltaic power generation into electrical power grids, there is still no benchmark study comparing a wide range of solar probabilistic methods across various local climates. Having identified this research gap, experts involved in the activities of IEA PVPS T16 1 1 International Energy Agency - Photovoltaic Power Systems - Solar Resource for High Penetration and Large Scale Applications. agreed to establish a benchmarking exercise to evaluate the quality of intra-hour and intra-day probabilistic irradiance forecasts. The tested forecasting methodologies are based on different input data including ground measurements, satellite-based forecasts and Numerical Weather Predictions (NWP), and different statistical methods are employed to generate probabilistic forecasts from these. The exercise highlights different forecast quality depending on the method used, and more importantly, on the input data fed into the models. In particular, the benchmarking procedure reveals that the association of a point forecast that blends ground, satellite and NWP data with a statistical technique generates high-quality probabilistic forecasts. Therefore, in a subsequent step, an additional investigation was conducted to assess the added value of such a blended point forecast on forecast quality. Three new statistical methods were implemented using the blended point forecast as input. To ensure a fair evaluation of the different methods, we calculate a skill score that measures the performance of the proposed model relative to that of a trivial baseline model. The closer the skill score is to 100%, the more efficient the method is. Overall, skill scores of methods that use the blended point forecast ranges from 42% to 46% for the intra-hour scenario and 27% to 32% for the intra-day scenario. Conversely, methods that do not use the blended point forecast exhibit skill scores ranging from 33% to 43% for intra-hour forecasts and 8% to 16% for intra-day forecasts. These results suggest that using (a) blended point forecasts that optimally combine different sources of input data and (b) a post-processing with a statistical method to produce the quantile forecasts is an effective and consistent way to generate high-quality intra-hour or intra-day probabilistic forecasts. [ABSTRACT FROM AUTHOR]

The "evoking freedom" technique consists in soliciting someone to comply with a request by simply saying that she is free to accept or to refuse the request. However, previous studies used low cost requests. The present study examined the magnitude of this technique associated with a more disturbing and costly request. Sixty men and 60 women aged approximately 20-25 years walking in the street were asked by a male confederate to hold a closed transparent box containing a live trap-door spider while he went into the post office to pick up a package. In the evoking freedom condition, the confederate added in his request that the participant was "free to accept or to refuse." More compliance occurred in the "evoking freedom" condition (53.3%) than in the control condition (36.7%). These results confirm the robustness and the magnitude of the evoking freedom technique on compliance and show that this technique remained effective even when the request was psychologically costly to perform and was associated with fear. [ABSTRACT FROM AUTHOR]

This paper presents a technico-economical analysis of a Pelamis wave power generator coupled with a proposed air compression storage system. Ocean wave measurements and forecasts are used from a site near the city of Saint-Pierre in Réunion island, France. The insular context requires both smoothing and forecast of the output power from the wave power system. The storage system is a solution to meet this requirement. Several power network services are defined by the utility operator in order to meet different load needs. The goal is to analyze the role of the proposed storage device for each desired network service. An optimization procedure, from previous works, based on available wave energy forecast, is used to compute the optimal storage size for each service. An economical analysis shows the feasibility from the addition of the storage device, as the hybrid source power output may be economically profitable compared to a raw wave power production. [ABSTRACT FROM AUTHOR]

In this paper, the performances of two approaches for solar probabilistic are evaluated using a set of metrics previously tested by the meteorology verification community. A particular focus is put on several scores and the decomposition of a specific probabilistic metric: the continuous rank probability score (CRPS) as they give extensive information to compare the forecasting performance of both methodologies. The two solar probabilistic forecasting methodologies are used to produce intra-day solar forecasts with time horizons ranging from 1 h to 6 h. The first methodology is based on two steps. In the first step, we generated a point forecast for each horizon and in a second step, we use quantile regression methods to estimate the prediction intervals. The second methodology directly estimates the prediction intervals of the forecasted clear sky index distribution using past data as inputs. With this second methodology we also propose to add solar geometric angles as inputs. Overall, nine probabilistic forecasting performances are compared at six measurements stations with different climatic conditions. This paper shows a detailed picture of the overall performance of the models and consequently may help in selecting the best methodology. [ABSTRACT FROM AUTHOR]

Based on the reported literature and commonly used metrics in the realm of solar forecasting, a new methodology is developed for estimating a metric called forecastability (F). It reveals the extent to which solar radiation time series can be forecasted and provides the crucial context for judging the inherent difficulty associated with a particular forecast situation. Unlike the score given by the standard smart persistence model, the F metric which is bounded between 0% and 100% is easier to interpret, hence making comparisons between forecasting studies more consistent. This approach uses the Monte Carlo method and estimates F from the standard error metric RMSE and the persistence predictor. Based on the time series of solar radiation measured at six very different locations (with optimized clear sky model) from a meteorological point of view, it is shown that F varies between 25.5% and 68.2% and that it exists a link between forecastability and errors obtained by machine learning prediction methods. The proposed methodology is validated for 3 parameters that may affect the F estimation (time horizon, temporal granularity, and solar radiation components) and for 50 time series relative to McClear web service and to the central archive of Baseline Surface Radiation Network. [ABSTRACT FROM AUTHOR]

• This review aims at standardizing the forecast verification approaches used in deterministic solar forecasting. • The distribution-oriented forecast verification framework is introduced. • RMSE skill score is recommended to be universally reported in solar forecasting studies. • A series of practical issues during verification are 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. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between two parameters characterizing a given location on a given day: the daily clear sky index KT ∗ and the intraday variability given by the standard deviation of the changes in the hourly clear sky index σ ( Δ kt Δ t ∗ ) . Empirical evidence assembled from twenty climatically distinct locations led us to derive a simple model to infer intraday variability from the day’s clear sky index. Although the model shows little dependence on location, we did observe a systematic difference traceable to a location’s prevailing cloud formation regime. Therefore, we also propose two alternative models for sites where cloud formations is influenced by local orography and sites where cloud formation is traceable to weather events only. Finally, we present a possible application of the models to enhance the informative content of day(s) ahead NWP forecasts. [ABSTRACT FROM AUTHOR]

Research has reported that the foot-in-the-door technique is effective at increasing helping behavior. However, the effect of this technique on negative social behavior has never been examined. A field experiment was conducted to explore whether this technique could reduce aggressiveness. Drivers waiting at a traffic light were blocked by an experimental car. In the Foot-in-the-door condition, when the traffic light was red, a passerby confederate asked the driver for directions to a well-known store located in the area of the experiment. The confederate then thanked the driver and walked off in the direction indicated. In the control condition, no request was addressed to the car driver. When the traffic light turned green, the experimental car pretended to be blocked by an engine problem. The number of drivers who honked at the target car and the amount of time that elapsed before the drivers responded by honking their horns were the dependent variables. It was found that fewer drivers honked in the Foot-in-the-door condition and drivers who honked displayed their behavior later than those in the control condition. Self-perception theory was used to explain these results. [ABSTRACT FROM AUTHOR]

Earlier research reported that using pictures in a fundraising request increases donations by legitimizing the solicitation. The present study tested the effectiveness of a solicitation for money made in the street when an object that legitimated the request was present or not. Male confederates asked passersby for money to buy a stamp while either holding or not holding an envelope in one hand. With a high effect-size, it was reported that the presence of the envelope increased compliance with the request. The legitimation of the request associated with the presence of the envelope is used to explain the results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a benchmarking of supervised machine learning techniques (neural networks, Gaussian processes and support vector machines) in order to forecast the Global Horizontal solar Irradiance (GHI). We also include in this benchmark a simple linear autoregressive (AR) model as well as two naive models based on persistence of the GHI and persistence of the clear sky index (denoted herein scaled persistence model). The models are calibrated and validated with data from three French islands: Corsica (41.91°N; 8.73°E), Guadeloupe (16.26°N; 61.51°W) and Reunion (21.34°S; 55.49°E). The main findings of this work are, that for hour ahead solar forecasting, the machine learning techniques slightly improve the performances exhibited by the linear AR and the scaled persistence model. However, the improvement appears to be more pronounced in case of unstable sky conditions. These nonlinear techniques start to outperform their simple counterparts for forecasting horizons greater than 1 h. [ABSTRACT FROM AUTHOR]

The physical interconnection for optical transport networks has critical relevance in the overall network performance and deployment costs. As telecommunication services and technologies evolve, the provisioning of higher capacity and reliability levels is becoming essential for the proper development of Next Generation Networks. Currently, there is a lack of specific procedures that describe the basic guidelines to design such networks better than "best possible performance for the lowest investment". Therefore, the research from different points of view will allow a broader space of possibilities when designing the physical network interconnection. This paper develops and presents a methodology in order to deal with aspects related to the interconnection problem of optical transport networks. This methodology is presented as independent puzzle pieces, covering diverse topics going from novel design criteria to well-known organized topologies. These can be used to investigate the influence of the physical interconnection of networks over their performance properties, and draw conclusions to improve the current decision support techniques related to this theme. In addition, several examples of the use of this methodology are presented. [ABSTRACT FROM AUTHOR]