Incremental forecaster using C–C algorithm to phase space reconstruction and broad learning network for short-term wind speed prediction
Authors:
- Shiting HuSHShiting Hu
- Xuefang XuXXXuefang Xu
- Mengdi LiMengdi Li
- Peiming ShiPSPeiming Shi
- Ruixiong LiRLRuixiong Li
- Shuying WangSWShuying Wang
- 14 November 2023
- journal article
- research article
- Published by Elsevier in Engineering Applications of Artificial Intelligence
- Vol. 128
Abstract
No abstract availableKeywords
This publication has 40 references indexed in Scilit:
- Wind speed forecasting approach based on Singular Spectrum Analysis and Adaptive Neuro Fuzzy Inference SystemRenewable Energy, 2018
- Short-term wind speed forecasting based on fast ensemble empirical mode decomposition, phase space reconstruction, sample entropy and improved back-propagation neural networkEnergy Conversion and Management, 2018
- Accurate photovoltaic power forecasting models using deep LSTM-RNNNeural Computing and Applications, 2017
- Deep learning based ensemble approach for probabilistic wind power forecastingApplied Energy, 2017
- Deep belief network based deterministic and probabilistic wind speed forecasting approachApplied Energy, 2016
- Wind Pattern Recognition and Reference Wind Mast Data Correlations With NWP for Improved Wind-Electric Power ForecastsIEEE Transactions on Industrial Informatics, 2016
- Phase space reconstruction for improving the classification of single trial EEGBiomedical Signal Processing and Control, 2014
- Forecasting wind speed with recurrent neural networksEuropean Journal of Operational Research, 2012
- Selection of Embedding Dimension and Delay Time in Phase Space ReconstructionFrontiers of Electrical and Electronic Engineering in China, 2006
- Forecast of hourly average wind speed with ARMA models in Navarre (Spain)Solar Energy, 2005