Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization
By Jie Chen, Guo-qiang Zeng, Wuneng Zhou, Wei Du, Kang-Di Lu. Energy Conversion and Management, 2018
378 citations recorded by the source corpus.
Abstract As an essential issue in wind energy industry, wind speed forecasting plays a vital role in optimal scheduling and control of wind energy generation and conversion. In this paper, a novel method called EnsemLSTM is proposed by using nonlinear-learning ensemble of deep learning time series prediction based on LSTMs (Long Short Term Memory neural networks), SVRM (support vector regression machine) and EO (extremal optimization algorithm). First, in order to avert the drawback of weak generalization capability and robustness of a single deep learning approach when facing diversiform data, a cluster of LSTMs with diverse hidden layers and neurons are employed to explore and exploit the implicit information of wind speed time series. Then predictions of LSTMs are aggregated into a nonlinear-learning regression top-layer composed of SVRM and the EO is introduced to optimize the parameters of the top-layer. Lastly, the final ensemble prediction for wind speed is given by the fine-turning top-layer. The proposed EnsemLSTM is applied on two case studies data collected from a wind farm in Inner Mongolia, China, to perform ten-minute ahead utmost short term wind speed forecasting and one-hour ahead short term wind speed forecasting. Statistical tests of experimental results compared with other popular prediction models demonstrated the proposed EnsemLSTM can achieve a better forecasting performance.
Subfields: Electrical and Electronic Engineering, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/fc8f2264c67107fe3ae5c084d436075afe6486b8
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Electrical and Electronic Engineering
In Management Science and Operations Research
Citations in the corpus
- Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model (2020, background): “2018), time series and spatiotemporal prediction (Chen et al. 2018; Yang and Chen 2019; Xu et al. 2019; Bui et al. 2019) and natural language processing (Shin et al.”
- A Survey on ensemble learning under the era of deep learning (2021, methodology): “For the landslide susceptibility mapping, Chen et al. (Chen et al. 2017) proposed several ensembles using multiple base learners that were heterogeneously trained with artificial neural network (ANN) (Rumelhart et al. 1986), maximum entropy (MaxEnt) (Phillips et al. 2004) and SVM (Chang and Lin 2011”