Offshore wind speed assessment with statistical and attention-based neural network methods based on STL decomposition

Li Xu, Yanxia Ou, Jingjing Cai, Jin Wang, Yang Fu and Xiaoyan Bian

Renewable Energy, 2023, vol. 216, issue C

Abstract: This work proposes a novel offshore wind speed prediction approach by combining statistical method and attention-based neural network with seasonal-trend decomposition procedure with loess (STL). STL is utilized to decompose the processed data into season, trend and residual terms. Then, an attention-based long short-term memory neural network model (AT-LSTM), possessing the advantages of generalization and high-dimensional function approximation, is modeled to train season and residual terms with obvious volatility characteristics. And a hybrid model of auto-regressive integrated moving average (ARIMA) and LSTM is applied to predict the linear and nonlinear sequences in trend term with relatively gentle feature, yielding the proposed STL-AR-LSTM-ATLSTM model. Wherein, the proposed method is verified through sufficient pre-judgment experiments on season, trend and residual terms, as well as detailed multi-model comparison experiments. Finally, microcosmic prediction results and predicted statistical frequency distributions indicate that the new model has better prediction effect on offshore wind, compared to ARIMA, AT-LSTM, ARIMA-AT-LSTM models. Meanwhile, the presented model can reduce the lag problem of predicted values and perform well in the prediction of extreme values.

Keywords: Offshore wind; Wind speed prediction; Statistical model; Attention-based neural network; Seasonal-trend decomposition procedure with loess (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:216:y:2023:i:c:s096014812301011x

DOI: 10.1016/j.renene.2023.119097

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