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      A PSO-LSTM Model of Offshore Wind Power Forecast considering the Variation of Wind Speed in Second-Level Time Scale

      1 , 1 , 1 , 1 , 1
      Mathematical Problems in Engineering
      Hindawi Limited

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          Abstract

          To enable power generation companies to make full use of effective wind energy resources and grid companies to correctly schedule wind power, this paper proposes a model of offshore wind power forecast considering the variation of wind speed in second-level time scale. First, data preprocessing is utilized to process the abnormal data and complete the normalization of offshore wind speed and wind power. Then, a wind speed prediction model is established in the second time scale through the differential smoothing power sequence. Finally, a rolling PSO-LSTM memory network is authorized to realize the prediction of second-level time scale wind speed and power. An offshore wind power case is utilized to illustrate and characterize the performance of the wind power forecast model.

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          Long Short-Term Memory

          Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
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            Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine

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              An Advanced Statistical Method for Wind Power Forecasting

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                Author and article information

                Contributors
                Journal
                Mathematical Problems in Engineering
                Mathematical Problems in Engineering
                Hindawi Limited
                1563-5147
                1024-123X
                November 25 2021
                November 25 2021
                : 2021
                : 1-9
                Affiliations
                [1 ]Jiangsu Frontier Electric Technology Co. Ltd, Nanjing 211102, China
                Article
                10.1155/2021/2009062
                daa04eb4-91ee-444b-a6e5-dfed602edcc8
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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