The Experts below are selected from a list of 255 Experts worldwide ranked by ideXlab platform

Marie Laure Nivet - One of the best experts on this subject based on the ideXlab platform.

  • Hybrid methodology for hourly global radiation forecasting in Mediterranean area
    Renewable Energy, 2013
    Co-Authors: Cyril Voyant, Marc Muselli, Christophe Paoli, Marie Laure Nivet
    Abstract:

    The renewable energies prediction and particularly global radiation forecasting is a challenge studied by a growing number of research teams. This paper proposes an original technique to model the insolation time series based on combining Artificial Neural Network (ANN) and Auto-Regressive and Moving Average (Arma) model. While ANN by its non-linear nature is effective to predict cloudy days, Arma techniques are more dedicated to sunny days without cloud occurrences. Thus, three hybrids models are suggested: the first proposes simply to use Arma for 6 months in spring and summer and to use an optimized ANN for the other part of the year; the second model is equivalent to the first but with a seasonal learning; the last model depends on the error occurred the previous hour. These models were used to forecast the hourly global radiation for five places in Mediterranean area. The forecasting performance was compared among several models: the 3 above mentioned models, the best ANN and Arma for each location. In the best configuration, the coupling of ANN and Arma allows an improvement of more than 1%, with a maximum in autumn (3.4%) and a minimum in winter (0.9%) where ANN alone is the best.

  • numerical weather prediction nwp and hybrid Arma ann model to predict global radiation
    Energy, 2012
    Co-Authors: Cyril Voyant, Marc Muselli, Christophe Paoli, Marie Laure Nivet
    Abstract:

    We propose in this paper an original technique to predict global radiation using a hybrid Arma/ANN model and data issued from a numerical weather prediction model (NWP). We particularly look at the multi-layer perceptron (MLP). After optimizing our architecture with NWP and endogenous data previously made stationary and using an innovative pre-input layer selection method, we combined it to an Arma model from a rule based on the analysis of hourly data series. This model has been used to forecast the hourly global radiation for five places in Mediterranean area. Our technique outperforms classical models for all the places. The nRMSE for our hybrid model MLP/Arma is 14.9% compared to 26.2% for the naive persistence predictor. Note that in the standalone ANN case the nRMSE is 18.4%. Finally, in order to discuss the reliability of the forecaster outputs, a complementary study concerning the confidence interval of each prediction is proposed.

  • Numerical Weather Prediction (NWP) and hybrid Arma/ANN model to predict global radiation
    Energy, 2012
    Co-Authors: Cyril Voyant, Marc Muselli, Christophe Paoli, Marie Laure Nivet
    Abstract:

    We propose in this paper an original technique to predict global radiation using a hybrid Arma/ANN model and data issued from a numerical weather prediction model (ALADIN). We particularly look at the Multi-Layer Perceptron. After optimizing our architecture with ALADIN and endogenous data previously made stationary and using an innovative pre-input layer selection method, we combined it to an Arma model from a rule based on the analysis of hourly data series. This model has been used to forecast the hourly global radiation for five places in Mediterranean area. Our technique outperforms classical models for all the places. The nRMSE for our hybrid model ANN/Arma is 14.9% compared to 26.2% for the naïve persistence predictor. Note that in the stand alone ANN case the nRMSE is 18.4%. Finally, in order to discuss the reliability of the forecaster outputs, a complementary study concerning the confidence interval of each prediction is proposed

Cyril Voyant - One of the best experts on this subject based on the ideXlab platform.

  • Hybrid methodology for hourly global radiation forecasting in Mediterranean area
    Renewable Energy, 2013
    Co-Authors: Cyril Voyant, Marc Muselli, Christophe Paoli, Marie Laure Nivet
    Abstract:

    The renewable energies prediction and particularly global radiation forecasting is a challenge studied by a growing number of research teams. This paper proposes an original technique to model the insolation time series based on combining Artificial Neural Network (ANN) and Auto-Regressive and Moving Average (Arma) model. While ANN by its non-linear nature is effective to predict cloudy days, Arma techniques are more dedicated to sunny days without cloud occurrences. Thus, three hybrids models are suggested: the first proposes simply to use Arma for 6 months in spring and summer and to use an optimized ANN for the other part of the year; the second model is equivalent to the first but with a seasonal learning; the last model depends on the error occurred the previous hour. These models were used to forecast the hourly global radiation for five places in Mediterranean area. The forecasting performance was compared among several models: the 3 above mentioned models, the best ANN and Arma for each location. In the best configuration, the coupling of ANN and Arma allows an improvement of more than 1%, with a maximum in autumn (3.4%) and a minimum in winter (0.9%) where ANN alone is the best.

  • numerical weather prediction nwp and hybrid Arma ann model to predict global radiation
    Energy, 2012
    Co-Authors: Cyril Voyant, Marc Muselli, Christophe Paoli, Marie Laure Nivet
    Abstract:

    We propose in this paper an original technique to predict global radiation using a hybrid Arma/ANN model and data issued from a numerical weather prediction model (NWP). We particularly look at the multi-layer perceptron (MLP). After optimizing our architecture with NWP and endogenous data previously made stationary and using an innovative pre-input layer selection method, we combined it to an Arma model from a rule based on the analysis of hourly data series. This model has been used to forecast the hourly global radiation for five places in Mediterranean area. Our technique outperforms classical models for all the places. The nRMSE for our hybrid model MLP/Arma is 14.9% compared to 26.2% for the naive persistence predictor. Note that in the standalone ANN case the nRMSE is 18.4%. Finally, in order to discuss the reliability of the forecaster outputs, a complementary study concerning the confidence interval of each prediction is proposed.

  • Numerical Weather Prediction (NWP) and hybrid Arma/ANN model to predict global radiation
    Energy, 2012
    Co-Authors: Cyril Voyant, Marc Muselli, Christophe Paoli, Marie Laure Nivet
    Abstract:

    We propose in this paper an original technique to predict global radiation using a hybrid Arma/ANN model and data issued from a numerical weather prediction model (ALADIN). We particularly look at the Multi-Layer Perceptron. After optimizing our architecture with ALADIN and endogenous data previously made stationary and using an innovative pre-input layer selection method, we combined it to an Arma model from a rule based on the analysis of hourly data series. This model has been used to forecast the hourly global radiation for five places in Mediterranean area. Our technique outperforms classical models for all the places. The nRMSE for our hybrid model ANN/Arma is 14.9% compared to 26.2% for the naïve persistence predictor. Note that in the stand alone ANN case the nRMSE is 18.4%. Finally, in order to discuss the reliability of the forecaster outputs, a complementary study concerning the confidence interval of each prediction is proposed

Nobuo Nagai - One of the best experts on this subject based on the ideXlab platform.

  • ICASSP - Area estimation from Arma analysis based on vocal-tract model
    ICASSP '86. IEEE International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: Nobuhiro Miki, S. Saga, K. Motoki, Yoshikazu Miyanaga, Nobuo Nagai
    Abstract:

    A vocal-tract model with boundary conditions is proposed, and it is shown that the model can be applied to a vocal-tract simulator add an estimation algorithm of vocal-tract shapes. Since our vocal-tract simulator can dynamically synthesize continuous speech with the vocal-fold vibration and the loss factor in the vocal-tract, this simulator can be used as a tool for accurate evaluation of the estimation accuracy of the formants or the characteristics of the vocal-tract. We propose a new method estimating cross-sectional area of vocal-tract from the Arma parameters, and this method is based on the vocal-tract model and our Arma estimation algorithms.

Marc Muselli - One of the best experts on this subject based on the ideXlab platform.

  • Hybrid methodology for hourly global radiation forecasting in Mediterranean area
    Renewable Energy, 2013
    Co-Authors: Cyril Voyant, Marc Muselli, Christophe Paoli, Marie Laure Nivet
    Abstract:

    The renewable energies prediction and particularly global radiation forecasting is a challenge studied by a growing number of research teams. This paper proposes an original technique to model the insolation time series based on combining Artificial Neural Network (ANN) and Auto-Regressive and Moving Average (Arma) model. While ANN by its non-linear nature is effective to predict cloudy days, Arma techniques are more dedicated to sunny days without cloud occurrences. Thus, three hybrids models are suggested: the first proposes simply to use Arma for 6 months in spring and summer and to use an optimized ANN for the other part of the year; the second model is equivalent to the first but with a seasonal learning; the last model depends on the error occurred the previous hour. These models were used to forecast the hourly global radiation for five places in Mediterranean area. The forecasting performance was compared among several models: the 3 above mentioned models, the best ANN and Arma for each location. In the best configuration, the coupling of ANN and Arma allows an improvement of more than 1%, with a maximum in autumn (3.4%) and a minimum in winter (0.9%) where ANN alone is the best.

  • numerical weather prediction nwp and hybrid Arma ann model to predict global radiation
    Energy, 2012
    Co-Authors: Cyril Voyant, Marc Muselli, Christophe Paoli, Marie Laure Nivet
    Abstract:

    We propose in this paper an original technique to predict global radiation using a hybrid Arma/ANN model and data issued from a numerical weather prediction model (NWP). We particularly look at the multi-layer perceptron (MLP). After optimizing our architecture with NWP and endogenous data previously made stationary and using an innovative pre-input layer selection method, we combined it to an Arma model from a rule based on the analysis of hourly data series. This model has been used to forecast the hourly global radiation for five places in Mediterranean area. Our technique outperforms classical models for all the places. The nRMSE for our hybrid model MLP/Arma is 14.9% compared to 26.2% for the naive persistence predictor. Note that in the standalone ANN case the nRMSE is 18.4%. Finally, in order to discuss the reliability of the forecaster outputs, a complementary study concerning the confidence interval of each prediction is proposed.

  • Numerical Weather Prediction (NWP) and hybrid Arma/ANN model to predict global radiation
    Energy, 2012
    Co-Authors: Cyril Voyant, Marc Muselli, Christophe Paoli, Marie Laure Nivet
    Abstract:

    We propose in this paper an original technique to predict global radiation using a hybrid Arma/ANN model and data issued from a numerical weather prediction model (ALADIN). We particularly look at the Multi-Layer Perceptron. After optimizing our architecture with ALADIN and endogenous data previously made stationary and using an innovative pre-input layer selection method, we combined it to an Arma model from a rule based on the analysis of hourly data series. This model has been used to forecast the hourly global radiation for five places in Mediterranean area. Our technique outperforms classical models for all the places. The nRMSE for our hybrid model ANN/Arma is 14.9% compared to 26.2% for the naïve persistence predictor. Note that in the stand alone ANN case the nRMSE is 18.4%. Finally, in order to discuss the reliability of the forecaster outputs, a complementary study concerning the confidence interval of each prediction is proposed

Christophe Paoli - One of the best experts on this subject based on the ideXlab platform.

  • Hybrid methodology for hourly global radiation forecasting in Mediterranean area
    Renewable Energy, 2013
    Co-Authors: Cyril Voyant, Marc Muselli, Christophe Paoli, Marie Laure Nivet
    Abstract:

    The renewable energies prediction and particularly global radiation forecasting is a challenge studied by a growing number of research teams. This paper proposes an original technique to model the insolation time series based on combining Artificial Neural Network (ANN) and Auto-Regressive and Moving Average (Arma) model. While ANN by its non-linear nature is effective to predict cloudy days, Arma techniques are more dedicated to sunny days without cloud occurrences. Thus, three hybrids models are suggested: the first proposes simply to use Arma for 6 months in spring and summer and to use an optimized ANN for the other part of the year; the second model is equivalent to the first but with a seasonal learning; the last model depends on the error occurred the previous hour. These models were used to forecast the hourly global radiation for five places in Mediterranean area. The forecasting performance was compared among several models: the 3 above mentioned models, the best ANN and Arma for each location. In the best configuration, the coupling of ANN and Arma allows an improvement of more than 1%, with a maximum in autumn (3.4%) and a minimum in winter (0.9%) where ANN alone is the best.

  • numerical weather prediction nwp and hybrid Arma ann model to predict global radiation
    Energy, 2012
    Co-Authors: Cyril Voyant, Marc Muselli, Christophe Paoli, Marie Laure Nivet
    Abstract:

    We propose in this paper an original technique to predict global radiation using a hybrid Arma/ANN model and data issued from a numerical weather prediction model (NWP). We particularly look at the multi-layer perceptron (MLP). After optimizing our architecture with NWP and endogenous data previously made stationary and using an innovative pre-input layer selection method, we combined it to an Arma model from a rule based on the analysis of hourly data series. This model has been used to forecast the hourly global radiation for five places in Mediterranean area. Our technique outperforms classical models for all the places. The nRMSE for our hybrid model MLP/Arma is 14.9% compared to 26.2% for the naive persistence predictor. Note that in the standalone ANN case the nRMSE is 18.4%. Finally, in order to discuss the reliability of the forecaster outputs, a complementary study concerning the confidence interval of each prediction is proposed.

  • Numerical Weather Prediction (NWP) and hybrid Arma/ANN model to predict global radiation
    Energy, 2012
    Co-Authors: Cyril Voyant, Marc Muselli, Christophe Paoli, Marie Laure Nivet
    Abstract:

    We propose in this paper an original technique to predict global radiation using a hybrid Arma/ANN model and data issued from a numerical weather prediction model (ALADIN). We particularly look at the Multi-Layer Perceptron. After optimizing our architecture with ALADIN and endogenous data previously made stationary and using an innovative pre-input layer selection method, we combined it to an Arma model from a rule based on the analysis of hourly data series. This model has been used to forecast the hourly global radiation for five places in Mediterranean area. Our technique outperforms classical models for all the places. The nRMSE for our hybrid model ANN/Arma is 14.9% compared to 26.2% for the naïve persistence predictor. Note that in the stand alone ANN case the nRMSE is 18.4%. Finally, in order to discuss the reliability of the forecaster outputs, a complementary study concerning the confidence interval of each prediction is proposed