A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting
Authors
Keywords
Wind energy, Forecasting, Time series, Decomposition, Stacking ensemble learning, Machine learning
Journal
ENERGY
Volume 216, Issue -, Pages 119174
Publisher
Elsevier BV
Online
2020-11-03
DOI
10.1016/j.energy.2020.119174
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Medium-term wind power forecasting based on multi-resolution multi-learner ensemble and adaptive model selection
- (2020) Chao Chen et al. ENERGY CONVERSION AND MANAGEMENT
- Wind power prediction using a three stage genetic ensemble and auxiliary predictor
- (2020) Farah Shahid et al. APPLIED SOFT COMPUTING
- Wind power forecasting using attention-based gated recurrent unit network
- (2020) Zhewen Niu et al. ENERGY
- A novel Deep Learning Framework: Prediction and Analysis of Financial Time Series using CEEMD and LSTM
- (2020) Yong'an Zhang et al. EXPERT SYSTEMS WITH APPLICATIONS
- Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts
- (2020) Mumtaz Ali et al. JOURNAL OF HYDROLOGY
- A filter-based feature construction and feature selection approach for classification using Genetic Programming
- (2020) Jianbin Ma et al. KNOWLEDGE-BASED SYSTEMS
- Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: A case study
- (2020) Dongxiao Niu et al. APPLIED SOFT COMPUTING
- Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil
- (2020) Matheus Henrique Dal Molin Ribeiro et al. CHAOS SOLITONS & FRACTALS
- Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network
- (2020) Zi Lin et al. ENERGY
- Combination of short-term load forecasting models based on a stacking ensemble approach
- (2020) Jihoon Moon et al. ENERGY AND BUILDINGS
- Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network
- (2020) Sinvaldo Rodrigues Moreno et al. ENERGY CONVERSION AND MANAGEMENT
- Multi-label feature selection with shared common mode
- (2020) Liang Hu et al. PATTERN RECOGNITION
- Research on short-term wind power combined forecasting and its Gaussian cloud uncertainty to support the integration of renewables and EVs
- (2020) Jinhua Zhang et al. RENEWABLE ENERGY
- Forecasting energy consumption and wind power generation using deep echo state network
- (2020) Huanling Hu et al. RENEWABLE ENERGY
- Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables
- (2020) Ramon Gomes da Silva et al. CHAOS SOLITONS & FRACTALS
- Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning
- (2020) Matheus Ribeiro et al. Energies
- A multi-objective wind speed and wind power prediction interval forecasting using variational modes decomposition based Multi-Kernel Robust Ridge regression
- (2019) Jyotirmayee Naik et al. RENEWABLE ENERGY
- An innovative hybrid system for wind speed forecasting based on fuzzy preprocessing scheme and multi-objective optimization
- (2019) Chen Li et al. ENERGY
- A Novel Hybrid System Based on Multi-objective Optimization for Wind Speed Forecasting
- (2019) Chunying Wu et al. RENEWABLE ENERGY
- Data processing strategies in wind energy forecasting models and applications: A comprehensive review
- (2019) Hui Liu et al. APPLIED ENERGY
- Prediction interval of wind power using parameter optimized Beta distribution based LSTM model
- (2019) Xiaohui Yuan et al. APPLIED SOFT COMPUTING
- Wind power forecasting based on singular spectrum analysis and a new hybrid Laguerre neural network
- (2019) Cong Wang et al. APPLIED ENERGY
- Wind power forecasting based on daily wind speed data using machine learning algorithms
- (2019) Halil Demolli et al. ENERGY CONVERSION AND MANAGEMENT
- Combined model with secondary decomposition-model selection and sample selection for multi-step wind power forecasting
- (2019) Zhuochun Wu et al. APPLIED ENERGY
- Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series
- (2019) Matheus Henrique Dal Molin Ribeiro et al. APPLIED SOFT COMPUTING
- Wind power forecast based on improved Long Short Term Memory network
- (2019) Li Han et al. ENERGY
- Day ahead powerful probabilistic wind power forecast using combined intelligent structure and fuzzy clustering algorithm
- (2019) Lei Li et al. ENERGY
- Corrected multi-resolution ensemble model for wind power forecasting with real-time decomposition and Bivariate Kernel density estimation
- (2019) Hui Liu et al. ENERGY CONVERSION AND MANAGEMENT
- Very short-term wind power density forecasting through artificial neural networks for microgrid control
- (2019) Fermín Rodríguez et al. RENEWABLE ENERGY
- Multi-distribution ensemble probabilistic wind power forecasting
- (2019) Mucun Sun et al. RENEWABLE ENERGY
- A novel wind power probabilistic forecasting approach based on joint quantile regression and multi-objective optimization
- (2019) Jianming Hu et al. RENEWABLE ENERGY
- Using dimension reduction PCA to identify ecosystem service bundles
- (2018) Cedric Marsboom et al. ECOLOGICAL INDICATORS
- A hybrid forecasting approach applied in wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm
- (2018) Zhongshan Yang et al. ENERGY
- Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method
- (2018) Deockho Kim et al. ENERGY
- Wind speed forecasting approach based on Singular Spectrum Analysis and Adaptive Neuro Fuzzy Inference System
- (2018) Sinvaldo Rodrigues Moreno et al. RENEWABLE ENERGY
- Short term wind power forecasting using hybrid variational mode decomposition and multi-kernel regularized pseudo inverse neural network
- (2018) Jyotirmayee Naik et al. RENEWABLE ENERGY
- Multi-step ahead forecasting in electrical power system using a hybrid forecasting system
- (2018) Pei Du et al. RENEWABLE ENERGY
- The impact of energy storage modeling in coordination with wind farm and thermal units on security and reliability in a stochastic unit commitment
- (2018) Mohsen Vatanpour et al. ENERGY
- Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting strategy
- (2018) Yanfei Li et al. RENEWABLE ENERGY
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExplorePublish 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 More