109 research outputs found
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
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
Kalman filtering and classical time series tools for global radiation prediction
For nowcasting and short term forecasting of salar irradiation, the usual technics are based on machine learning predictions such as Artificial Neural Network (ANN) [1], Support Vector Machines (SVM) [2], AutoRegressive–Moving-Average (ARMA) models [3], etc. A significant inconvenience of these methods is related to the large historic data set required during the training phase of the predictors; thus, in this work, we propose a simple methodology able to predict a global radiation time series without the need of historical data, making the method easily applicable for poor instrumented areas. We suggest to call these intuitive methods in the following “training-less” methods. The accuracy of these methods will be compared against other classical prediction methods, taking into account the time horizon of the prediction
Numerical weather prediction or stochastic modeling: an objective criterion of choice for the global radiation forecasting
Numerous methods exist and were developed for global radiation forecasting.
The two most popular types are the numerical weather predictions (NWP) and the
predictions using stochastic approaches. We propose to compute a parameter
noted constructed in part from the mutual information which is a quantity that
measures the mutual dependence of two variables. Both of these are calculated
with the objective to establish the more relevant method between NWP and
stochastic models concerning the current problem.Comment: International Journal of Energy Technology and Policy (2014
The electrical energy situation of French islands and focus on the Corsican situation
The present work aims to present the electrical energy situation of several French islands spread over the World. Various aspects are successively studied: repartition of energy means, renewable energy part in the production with a focus on the intermittent renewable sources, legal and financial aspect. The electrical situation of the islands is compared with the French mainland one. The electricity production cost in the islands are presented and the financial features for renewable energy in France are exposed. In a second part, a focus is realized on the Corsica Island situated in the Mediterranean Sea and partially connected to Italy. Successively, the energy mix, the objective of the new energy plan for 2023 and the renewable energy situation, present and future, are presented. Even if the integration of non-programmable renewable energy plants is more complex in small insular networks, the high cost of electricity generation in such territories encourages the introduction of wind and PV systems. The islands are good laboratories for the development of intermittent and stochastic renewable energy systems
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