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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

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

Programa de doctorado: Tecnología Industrial. La fecha de publicación es la fecha de lectura. [ES]La radiación solar global es una de las variables más importantes para determinar la capacidad de producción eléctrica de una región. Una predicción fiable de las condiciones de radiación solar pueden conducir a un aprovechamiento más eficiente de la energía producida en las centrales solares. La energía proveniente de fuentes convencionales es fácilmente regulable, por lo que disponer de los patrones de carga y producción de energía solar con adelanto permitirá a los operadores un mejor balance entre la demanda y la generación de potencia. En los sistemas insulares, como es el caso de Gran Canaria, estos problemas se agudizan al no encontrarse conectados con la red general eléctrica del continente.

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

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.

International audience; In this paper, we propose a new pruning algorithm to obtain the optimal number of hidden units of a single layer of a fully connected neural network (NN). The technique relies on a global sensitivity analysis of model output. The relevance of the hidden nodes is determined by analysing the Fourier decomposition of the variance of the model output. Each hidden unit is assigned a ratio (the fraction of variance which the unit accounts for) that gives their ranking. This quantitative information therefore leads to a suggestion of the most favorable units to eliminate. Experimental results suggest that the method can be seen as an effective tool available to the user in controlling the complexity in NNs.

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.

Because of increasing integration of solar energy into the electrical network solar irradiance forecasting is becoming essential. In fact, this integration can offer a better quality of service if the solar irradiation variation can be predicted with great accuracy. This paper presents an in-depth review of the current methods used to forecast solar irradiation in order to facilitate selection of forecast method according to needs. Our work starts with a presentation of physical models and techniques based on cloud imagery. Next statistical approaches are detailed before discussing hybrid models. Finally, we give indications for future solar irradiation forecasting approaches dedicated to the management of small-scale insular grids. Refereed/Peer-reviewed

We propose a Bayesian statistical approach to deriving a simple logistic hourly diffuse fraction model. The model is calibrated with data that include northern as well as southern hemisphere sites. An independent dataset comprising seven worldwide locations is used to compare the model against several previous models such as Skartveit et al. (1998), Reindl et al. (1990), Erbs et al. (1982), and a version of the logistic model derived by Boland et al. (2008). On an overall basis, the new model performs better than the models of Erbs or Reindl, and exhibits similar performance to the Skartveit model but with a much simpler expression. In addition, the use of a Bayesian criterion for model selection confirms that the new proposed model achieves the best trade-off between goodness-of-fit and model complexity. Finally, it is shown that the use of Bayesian methods instead of classical statistical techniques lead to a less-biased model. Our presentation is accessible to readers with an intermediate level of statistics. Refereed/Peer-reviewed