Solar irradiation nowcasting by stochastic persistence: a new parsimonious, simple and efficient forecasting tool

International audienceSimple, naïve, smart or clearness persistences are tools largely used as naïve predictors for the global solar irradiation forecasting. It is essential to compare the performances of sophisticated prediction approaches with that of a reference approach generally a naïve methods. In this paper, a new kind of naïve " nowcaster " is developed, a persistence model based on the stochastic aspect of measured solar energy signal denoted stochastic persistence and constructed without needing a large collection of historical data. Two versions are proposed: one based on an additive and one on a multiplicative scheme; a theoretical description and an experimental validation based on measurements realized in Ajaccio (France) and Tilos (Greece) are exposed. The results show that this approach is efficient, easy to implement and does not need historical data as the machine learning methods usually employed. This new solar irradiation predictor could become an interesting tool and become a new member of the solar forecasting family

Meteorological time series forecasting based on MLP modelling using heterogeneous transfer functions

In this paper, we propose to study four meteorological and seasonal time series coupled with a multi-layer perceptron (MLP) modeling. We chose to combine two transfer functions for the nodes of the hidden layer, and to use a temporal indicator (time index as input) in order to take into account the seasonal aspect of the studied time series. The results of the prediction concern two years of measurements and the learning step, eight independent years. We show that this methodology can improve the accuracy of meteorological data estimation compared to a classical MLP modelling with a homogenous transfer function

Bayesian rules and stochastic models for high accuracy prediction of solar radiation

It is essential to find solar predictive methods to massively insert renewable energies on the electrical distribution grid. The goal of this study is to find the best methodology allowing predicting with high accuracy the hourly global radiation. The knowledge of this quantity is essential for the grid manager or the private PV producer in order to anticipate fluctuations related to clouds occurrences and to stabilize the injected PV power. In this paper, we test both methodologies: single and hybrid predictors. In the first class, we include the multi-layer perceptron (MLP), auto-regressive and moving average (ARMA), and persistence models. In the second class, we mix these predictors with Bayesian rules to obtain ad-hoc models selections, and Bayesian averages of outputs related to single models. If MLP and ARMA are equivalent (nRMSE close to 40.5% for the both), this hybridization allows a nRMSE gain upper than 14 percentage points compared to the persistence estimation (nRMSE=37% versus 51%).Comment: Applied Energy (2013

Solar energy production: Short-term forecasting and risk management

International audienceElectricity production via solar energy is tackled via short-term forecasts and risk management. Our main tool is a new setting on time series. It allows the definition of "confidence bands" where the Gaussian assumption, which is not satisfied by our concrete data, may be abandoned. Those bands are quite convenient and easily implementable. Numerous computer simulations are presented

Urban ozone concentration forecasting with artificial neural network in Corsica

Atmospheric pollutants concentration forecasting is an important issue in air quality monitoring. Qualitair Corse, the organization responsible for monitoring air quality in Corsica (France) region, needs to develop a short-term prediction model to lead its mission of information towards the public. Various deterministic models exist for meso-scale or local forecasting, but need powerful large variable sets, a good knowledge of atmospheric processes, and can be inaccurate because of local climatical or geographical particularities, as observed in Corsica, a mountainous island located in a Mediterranean Sea. As a result, we focus in this study on statistical models, and particularly Artificial Neural Networks (ANN) that have shown good results in the prediction of ozone concentration at horizon h+1 with data measured locally. The purpose of this study is to build a predictor to realize predictions of ozone and PM10 at horizon d+1 in Corsica in order to be able to anticipate pollution peak formation and to take appropriated prevention measures. Specific meteorological conditions are known to lead to particular pollution event in Corsica (e.g. Saharan dust event). Therefore, several ANN models will be used, for meteorological conditions clustering and for operational forecasting.Comment: Sustainable Solutions for Energy and Environment. EENVIRO 2013, Buchatrest : Romania (2013

Meteorological time series forecasting with pruned multi-layer perceptron and 2-stage Levenberg-Marquardt method

A Multi-Layer Perceptron (MLP) defines a family of artificial neural networks often used in TS modeling and forecasting. Because of its "black box" aspect, many researchers refuse to use it. Moreover, the optimization (often based on the exhaustive approach where "all" configurations are tested) and learning phases of this artificial intelligence tool (often based on the Levenberg-Marquardt algorithm; LMA) are weaknesses of this approach (exhaustively and local minima). These two tasks must be repeated depending on the knowledge of each new problem studied, making the process, long, laborious and not systematically robust. In this paper a pruning process is proposed. This method allows, during the training phase, to carry out an inputs selecting method activating (or not) inter-nodes connections in order to verify if forecasting is improved. We propose to use iteratively the popular damped least-squares method to activate inputs and neurons. A first pass is applied to 10% of the learning sample to determine weights significantly different from 0 and delete other. Then a classical batch process based on LMA is used with the new MLP. The validation is done using 25 measured meteorological TS and cross-comparing the prediction results of the classical LMA and the 2-stage LMA.Comment: International Journal of Modelling, Identification and Control (2014). arXiv admin note: substantial text overlap with arXiv:1308.194

Statistical parameters as a means to a priori assess the accuracy of solar forecasting models

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Biological effects and equivalent doses in radiotherapy: a software solution

The limits of TDF (time, dose, and fractionation) and linear quadratic models have been known for a long time. Medical physicists and physicians are required to provide fast and reliable interpretations regarding the delivered doses or any future prescriptions relating to treatment changes. We therefore propose a calculation interface under the GNU license to be used for equivalent doses, biological doses, and normal tumor complication probability (Lyman model). The methodology used draws from several sources: the linear-quadratic-linear model of Astrahan, the repopulation effects of Dale, and the prediction of multi-fractionated treatments of Thames. The results are obtained from an algorithm that minimizes an ad-hoc cost function, and then compared to the equivalent dose computed using standard calculators in seven French radiotherapy centers