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A Novel Correlation-optimized Deep Learning Method for Wind Speed Forecast
- Publication Year :
- 2023
-
Abstract
- The increasing installation rate of wind power poses great challenges to the global power system. In order to ensure the reliable operation of the power system, it is necessary to accurately forecast the wind speed and power of the wind turbines. At present, deep learning is progressively applied to the wind speed prediction. Nevertheless, the recent deep learning methods still reflect the embarrassment for practical applications due to model interpretability and hardware limitation. To this end, a novel deep knowledge-based learning method is proposed in this paper. The proposed method hybridizes pre-training method and auto-encoder structure to improve data representation and modeling of the deep knowledge-based learning framework. In order to form knowledge and corresponding absorbers, the original data is preprocessed by an optimization model based on correlation to construct multi-layer networks (knowledge) which are absorbed by sequence to sequence (Seq2Seq) models. Specifically, new cognition and memory units (CMU) are designed to reinforce traditional deep learning framework. Finally, the effectiveness of the proposed method is verified by three wind prediction cases from a wind farm in Liaoning, China. Experimental results show that the proposed method increases the stability and training efficiency compared to the traditional LSTM method and LSTM/GRU-based Seq2Seq method for applications of wind speed forecasting.
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Artificial Intelligence (cs.AI)
Optimization and Control (math.OC)
Computer Science - Artificial Intelligence
FOS: Electrical engineering, electronic engineering, information engineering
FOS: Mathematics
Systems and Control (eess.SY)
Electrical Engineering and Systems Science - Systems and Control
Mathematics - Optimization and Control
Machine Learning (cs.LG)
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....67b0d3ecb39634089e1bbd80435d18d9