Hybrid wind speed prediction framework using data pre-processing strategy based autoencoder network
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Hybrid wind speed prediction framework using data pre-processing strategy based autoencoder network
Authors
Keywords
Bidirectional-long short-term memory network (Bi-LSTM), Convolutional neural network (CNN), Forecasting, Improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), Uncertainty, Wind
Journal
ELECTRIC POWER SYSTEMS RESEARCH
Volume 206, Issue -, Pages 107821
Publisher
Elsevier BV
Online
2022-01-29
DOI
10.1016/j.epsr.2022.107821
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Application of hybrid model based on empirical mode decomposition, novel recurrent neural networks and the ARIMA to wind speed prediction
- (2021) Ming-De Liu et al. ENERGY CONVERSION AND MANAGEMENT
- Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks
- (2021) K.U. Jaseena et al. ENERGY CONVERSION AND MANAGEMENT
- Design of a combined system based on two-stage data preprocessing and multi-objective optimization for wind speed prediction
- (2021) Ying Wang et al. ENERGY
- Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM
- (2021) Xiaolei Liu et al. ENERGY
- Two-phase deep learning model for short-term wind direction forecasting
- (2021) Zhenhao Tang et al. RENEWABLE ENERGY
- A novel hybrid framework for wind speed forecasting using autoencoder‐based convolutional long short‐term memory network
- (2021) Vishalteja Kosana et al. International Transactions on Electrical Energy Systems
- Short-term probabilistic predictions of wind multi-parameter based on one-dimensional convolutional neural network with attention mechanism and multivariate copula distribution estimation
- (2021) Xinyu Zhao et al. ENERGY
- A novel combined model for wind speed prediction – Combination of linear model, shallow neural networks, and deep learning approaches
- (2021) Shuai Wang et al. ENERGY
- Short-term wind power forecasting by stacked recurrent neural networks with parametric sine activation function
- (2020) Xin Liu et al. ELECTRIC POWER SYSTEMS RESEARCH
- Temporal convolutional networks interval prediction model for wind speed forecasting
- (2020) Zhenhao Gan et al. ELECTRIC POWER SYSTEMS RESEARCH
- A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting
- (2020) Ping Jiang et al. ENERGY
- Short-term wind speed forecasting using recurrent neural networks with error correction
- (2020) Jikai Duan et al. ENERGY
- A novel deep interval prediction model with adaptive interval construction strategy and automatic hyperparameter tuning for wind speed forecasting
- (2020) Yuying Xie et al. ENERGY
- A self-organizing forecast of day-ahead wind speed: Selective ensemble strategy based on numerical weather predictions
- (2020) Jing Zhao et al. ENERGY
- Current status of hybrid structures in wind forecasting
- (2020) Mehrnaz Ahmadi et al. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
- Hybrid multi-stage decomposition with parametric model applied to wind speed forecasting in Brazilian Northeast
- (2020) Sinvaldo Rodrigues Moreno et al. RENEWABLE ENERGY
- 1D convolutional neural networks and applications: A survey
- (2020) Serkan Kiranyaz et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting
- (2019) Aasim et al. RENEWABLE ENERGY
- Inverse Burr distribution for extreme wind speed prediction: Genesis, identification and estimation
- (2016) Elio Chiodo et al. ELECTRIC POWER SYSTEMS RESEARCH
- ANN-based scenario generation methodology for stochastic variables of electric power systems
- (2016) Stylianos I. Vagropoulos et al. ELECTRIC POWER SYSTEMS RESEARCH
- ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation
- (2016) M. Hadi Amini et al. ELECTRIC POWER SYSTEMS RESEARCH
- Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm
- (2013) Da Liu et al. RENEWABLE ENERGY
- Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction
- (2012) Hui Liu et al. APPLIED ENERGY
- Short-term wind power forecasting using ridgelet neural network
- (2011) Nima Amjady et al. ELECTRIC POWER SYSTEMS RESEARCH
- Application of Auto-Regressive Models to U.K. Wind Speed Data for Power System Impact Studies
- (2011) David C. Hill et al. IEEE Transactions on Sustainable Energy
- Overview of wind power intermittency impacts on power systems
- (2009) M.H. Albadi et al. ELECTRIC POWER SYSTEMS RESEARCH
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreFind the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
Search