Abstract
High-precision forecasting of short-term wind power (WP) is integral for wind farms, the safe dispatch of power systems, and the stable operation of the power grid. Currently, the data related to the operation and maintenance of wind farms mainly comes from the Supervisory Control and Data Acquisition (SCADA) systems, with certain information about the operating characteristics of wind turbines being readable in the SCADA data. In short-term WP forecasting, Long Short-Term Memory (LSTM) is a commonly used in-depth learning method. In the present study, an optimized LSTM based on the modified bald eagle search (MBES) algorithm was established to construct an MBES-LSTM model, a short-term WP forecasting model to make predictions, so as to address the problem that the selection of LSTM hyperparameters may affect the forecasting results. After preprocessing the WP data acquired by SCADA, the MBES-LSTM model was used to forecast the WP. The experimental results reveal that, compared with the PSO-RBF, PSO-SVM, LSTM, PSO-LSTM, and BES-LSTM forecasting models, the MBES-LSTM model could effectively improve the accuracy of WP forecasting for wind farms.
Highlights
The mean square error obtained by training the Long Short-Term Memory (LSTM) network was used as the fitness value, and the value was updated in real-time as the bald eagle continued to operate; within the iteration range, Formulas
To better assess the performance of the modified bald eagle search (MBES)-LSTM forecasting model, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentile error (MAPE), coefficient of variance (COV), CC, Theil’s inequality coefficient (TIC), efficiency coefficient (EC), and r2 were used in the present study
An observation can be made that the forecasting value line of the MBES-LSTM model was closer to the green line, which indicates that the forecasting precision of MBES-LSTM was the highest
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. (2) Input gate: The function of the input gate is to establish a new unit state ce, perform where W f represents the weight matrix of the forget gate; B f represents the bias term; related processing therein, and control how much information is added. The calcucet = and tanhcontrol ht−1 , much xt ] + B (Wc × [how c) lation formula is as follows: where it is the input gate result; Wi represents the weight matrix of the input gate; Bi represents the bias of the input gate; current input cell state; Wc represents it =ce represents. (3) Output gate: In the output gate, the output ht −1 of the previous moment t − 1 and where Wo represents the weight matrix of the output gate; and Bo represents the bias term the input x of the current moment t are used to output f t through a sigmoid function of the outputt gate.
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