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An encoder-decoder model with dual-attention mechanism for wind power forecasting
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Publication Year
2022-01-01
Journal
IEEE Power and Energy Society General Meeting
Publisher
IEEE Computer Society
Citation
IEEE Power and Energy Society General Meeting, Vol.2022-July
Keyword
attention mechanismBayesian optimizationdual-attention mechanismencoder-decoderLSTMwind power forecasting
Mesh Keyword
Attention mechanismsAttention modelBayesian optimizationDual-attention mechanismEncoder-decoderHyper-parameterLearning modelsOptimization techniquesWind power forecasting
All Science Classification Codes (ASJC)
Energy Engineering and Power TechnologyNuclear Energy and EngineeringRenewable Energy, Sustainability and the EnvironmentElectrical and Electronic Engineering
Abstract
Forecasting of intermittent wind power is one of the fundamental challenges for the reliable and controllable operation of systems with wind power. This paper proposes a deep learning model i.e. a dual-attention based encoder-decoder model over Long Short-Term Memory (LSTM) for wind power forecasting. To get the appropriate combination of hyper-parameters, Bayesian optimization technique is implemented on the proposed dual-attention model. To check the accuracy of the proposed model, it has been compared with various state-of-the-art wind power forecasting models. In terms of different error metrics, the proposed model shown better efficiency than the other models. Furthermore, the attention layer's performance against important features have also been analyzed.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36829
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85141491026&origin=inward
DOI
https://doi.org/10.1109/pesgm48719.2022.9916813
Journal URL
http://ieeexplore.ieee.org/xpl/conferences.jsp
Type
Conference
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Jung, Jaesung  Image
Jung, Jaesung 정재성
Department of Electrical and Computer Engineering
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