Short-term average wind speed and turbulent standard deviation forecasts based on one-dimensional convolutional neural network and the integrate method for probabilistic framework. (1st January 2020)

• Record Type: Journal Article
• Authors: Zhao, Xinyu; Jiang, Na; Liu, Jinfu; Yu, Daren; Chang, Juntao
• Is Part Of: Energy conversion and management. Volume 203(2020)
• Publication Date: 2020-01-01
• Subjects: WSF wind speed forecast -- SSA singular spectrum analysis -- ELM Extreme Learning Machine -- EWT Empirical Wavelet Transform -- LSTM Long Short Term Memory -- EMD Empirical mode decomposition -- ENN Elman neural network -- SVR Support Vector Regression -- EEL Extreme Learning Machine-Elman Neural Network-Long Short Term Memory Neural Network -- CFD Computational Fluid Dynamics -- ANN artificial neural network; Turbulent standard deviation -- Correlation analysis -- 1-Dimensional CNN -- Multi-model forecasts -- Probabilistic wind speed -- Quantile statistics; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2019.112239
• Languages: English
• ISSNs: 0196-8904

Abstract

Graphical abstract: Highlights: Multi-model forecast method of average wind speed and turbulent standard deviation. Adaptive application of one-dimensional convolutional neural network. Creative probabilistic wind speed forecast work based on turbulent standard deviation. Demand-orientated case studies for 4 h ahead wind speed prediction of 16 steps. Abstract: Accurate wind speed forecast can provide important information for power system dispatching. Many studies focus on this topic over the last decades, but it's consistently a tough issue due to the intense uncertainty of wind. In physical perspective, wind speed consists of average component and turbulent component. Therefore, oriented to actual demand and improve the forecast precision, this paper develops a novel data-driven method to realize short-term combination forecasts of average wind speed and wind turbulent standard deviation. According to atmospheric boundary layer theory and correlation analysis of the two prediction targets, their time-delay items are combined as the model input features. One-dimensional convolutional neural network is innovatively applied for this work to excavate the timing coupled information in data. Well-performed models can be established after adequate training and validation. Inspired by Pauta criterion, adaptive parameter named as "turbulent standard deviation multiplicator" is defined, which is the specific value of predicted average wind speed error and wind turbulent standard deviation. It is decided as the medium to extend the study to probabilistic framework. Based on its quantile analysis, the statistical significance of the parameter is verified and the prediction results can be integrated to achieve 4 h ahead probabilistic wind speed forecasts. Actual data from China wind farm is utilized to execute the case experiments. Superior performances indicate the feasibility and effectiveness of proposed method and the uncertainties of wind are better learned.