A hybrid model with combined feature selection based on optimized VMD and improved multi-objective coati optimization algorithm for short-term wind power prediction
Authors:
- Chao WangCWChao Wang
- Hong LinHLHong Lin
- Heng HuHHHeng Hu
- Ming YangMYMing Yang
- Li MaLMLi Ma
Abstract
No abstract availableKeywords
This publication has 53 references indexed in Scilit:
- A review on multi-objective optimization framework in wind energy forecasting techniques and applicationsEnergy Conversion and Management, 2020
- Day-ahead spatiotemporal wind speed forecasting using robust design-based deep learning neural networkEnergy, 2020
- Probabilistic forecasting with temporal convolutional neural networkNeurocomputing, 2020
- Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network (GCN)Transportation Research Part C: Emerging Technologies, 2020
- Data processing strategies in wind energy forecasting models and applications: A comprehensive reviewApplied Energy, 2019
- Application of support vector machine models for forecasting solar and wind energy resources: A reviewJournal of Cleaner Production, 2018
- Forecasting Electricity Generation Capacity in Malaysia: An Auto Regressive Integrated Moving Average ApproachEnergy Procedia, 2017
- A new intelligent method based on combination of VMD and ELM for short term wind power forecastingNeurocomputing, 2016
- An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speedApplied Energy, 2016
- Variational Mode DecompositionIEEE Transactions on Signal Processing, 2013