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    A new prediction model based on cascade NN for wind power prediction

    , Article Computational Economics ; March , 2018 , Pages 1-25 ; 09277099 (ISSN) Torabi, A ; Kiaian Mousavy, S. A ; Dashti, V ; Saeedi, M ; Yousefi, N ; Sharif University of Technology
    Springer New York LLC  2018
    Abstract
    This paper presents a new prediction model based on empirical mode decomposition, feature selection and hybrid forecast engine. The whole structure of proposed model is based on nonstationarity and non-convex nature of wind power signal. The hybrid forecast engine consists of three main stages as; empirical mode decomposition, an intelligent algorithm and three stage neural network. All parameters of proposed neural network will be optimized by intelligent algorithm. Effectiveness of the proposed model is tested with real-world hourly data of wind farms in Canada, Spain and Texas. In order to demonstrate the validity of the proposed model, it is compared with several other wind speed and... 

    A new prediction model based on cascade NN for wind power prediction

    , Article Computational Economics ; Volume 53, Issue 3 , 2019 , Pages 1219-1243 ; 09277099 (ISSN) Torabi, A ; Kiaian Mousavy, S. A ; Dashti, V ; Saeedi, M ; Yousefi, N ; Sharif University of Technology
    Springer New York LLC  2019
    Abstract
    This paper presents a new prediction model based on empirical mode decomposition, feature selection and hybrid forecast engine. The whole structure of proposed model is based on nonstationarity and non-convex nature of wind power signal. The hybrid forecast engine consists of three main stages as; empirical mode decomposition, an intelligent algorithm and three stage neural network. All parameters of proposed neural network will be optimized by intelligent algorithm. Effectiveness of the proposed model is tested with real-world hourly data of wind farms in Canada, Spain and Texas. In order to demonstrate the validity of the proposed model, it is compared with several other wind speed and... 

    A high-accuracy hybrid method for short-term wind power forecasting

    , Article Energy ; Volume 238 , 2022 ; 03605442 (ISSN) Khazaei, S ; Ehsan, M ; Soleymani, S ; Mohammadnezhad Shourkaei, H ; Sharif University of Technology
    Elsevier Ltd  2022
    Abstract
    In this article, a high-accuracy hybrid approach for short-term wind power forecasting is proposed using historical data of wind farm and Numerical Weather Prediction (NWP) data. The power forecasting is carried out in three stages: wind direction forecasting, wind speed forecasting, and wind power forecasting. In all three phases, the same hybrid method is used, and the only difference is in the input data set. The main steps of the proposed method are constituted of outlier detection, decomposition of time series using wavelet transform, effective feature selection and prediction of each time series decomposed using Multilayer Perceptron (MLP) neural network. The combination of automatic...