Forecasting ultra‐short‐term wind power by multiview gated recurrent unit neural network

Abstract

Wind power generation prediction plays an important role in the safety and economic operation of the power system. There are many parameters recorded in wind farm data, such as wind power, wind speed, wind direction, and so on. Traditional wind power prediction modeling methods lack the mining of these parameter data and fail to make good use of some potential physical information. To address this challenge, this paper proposes a multiview neural network learning framework to predict wind power. One is the data attribute view of wind power, which uses the historical data feature of the wind power itself to learn the future wind power feature. The other is the physical attribute view of wind power, which uses the physical attribute features associated with the wind power definition to learn the future features. Then all the learned features are jointly fused to predict the future wind power values. In addition, an uncertain factor is proposed and computed, which is inspired by the wind power formula and usually associated with internal and external environment perturbation. All time series features are input into the gated recurrent unit neural network to form a hybrid neural network framework for wind power prediction. Experimental results under the measured condition and the standard condition of wind farms demonstrate the effectiveness of the proposed method.

GCNInformer: A combined deep learning model based on GCN and Informer for wind power forecasting

“…The Transformer model for time-series data prediction was first presented by Wu et al 31 Utilizing the self-attention mechanism effectively captures intricate dynamics and patterns within the time-series data. The Informer model, which accelerates inference for long series prediction tasks, was introduced by Zhou et al 40 An architecture for multiview neural networks was presented by Xiong et al 41 They employed gated recurrent unit neural networks to extract physical features and analyzed the patterns between these physical features and wind power. Table 1 provides a summary of the combinatorial models.…”

Wind power output prediction: a comparative study of extreme learning machine

“…Against the backdrop of the existing energy structure, the wind power's proportion of the power grid will continue to increase (Abdulrazaq and Vural, 2022;Xiong et al, 2022). Unlike traditional thermal power units, wind power output is influenced by external factors such as aging of internal devices, geographical environment, and meteorological conditions (Catalao et al, 2011).…”