Short-Term Wind Power Forecasting Based on VMD and a Hybrid SSA-TCN-BiGRU Network

Abstract

Wind power generation is a renewable energy source, and its power output is influenced by multiple factors such as wind speed, direction, meteorological conditions, and the characteristics of wind turbines. Therefore, accurately predicting wind power is crucial for the grid operation and maintenance management of wind power plants. This paper proposes a hybrid model to improve the accuracy of wind power prediction. Accurate wind power forecasting is critical for the safe operation of power systems. To improve the accuracy of wind power prediction, this paper proposes a hybrid model incorporating variational modal decomposition (VMD), a Sparrow Search Algorithm (SSA), and a temporal-convolutional-network-based bi-directional gated recurrent unit (TCN-BiGRU). The model first uses VMD to break down the raw power data into several modal components, and then it builds an SSA-TCN-BIGRU model for each component for prediction, and finally, it accumulates all the predicted components to obtain the wind power prediction results. The proposed short-term wind power prediction model was validated using measured data from a wind farm in China. The proposed VMD-SSA-TCN-BiGRU forecasting framework is compared with benchmark models to verify its practicability and reliability. Compared with the TCN-BiGRU, the symmetric mean absolute percentage error, the mean absolute error, and the root mean square error of the VMD-SSA-TCN-BiGRU model reduced by 34.36%, 49.14%, and 55.94%.

Multi-Step Short-Term Wind Power Prediction Model Based on CEEMD and Improved Snake Optimization Algorithm

“…Therefore, a hybrid approach combining data preprocessing methods with prediction models has emerged as the prevailing method for wind power prediction. Currently, widely used data preprocessing methods include Wavelet Transform (WT) [24], Variable Mode Decomposition (VMD) [25], Empirical Mode Decomposition (EMD) [26], Ensemble Empirical Mode Decomposition (EEMD) [27], and Complementary Ensemble Empirical Mode Decomposition (CEEMD) [28]. For example, literature [29] proposes an improved artificial bee colony algorithm based on Wavelet Transform (WT) combined with a kernel-limit learning machine, and experiments prove that the model can effectively improve the prediction accuracy of short-term wind power.…”

Short-term Wind Power Forecasts based on VMD-KPCA and CFSBOA-BiLSTM

“…[7] used integrated empirical mode decomposition (EEMD) to transform irregular wind power time series into relatively easy to analyze subseries, and the resulting smooth subseries were predicted using the least absolute shrinkage selection operator-quantile regression neural network (LASSO-QRNN) model. A hybrid model combining variational modal decomposition (VMD), sparrow search algorithm (SSA), and time convolutional network-based bi-directional gated recurrent unit (TCN-BiGRU) was proposed in [8]. These methods all involve decomposing wind power time series into components with different frequencies, predicting these components, and combining the predicted values to obtain the final result.…”

Prediction of Energy Consumption in a Coal-Fired Boiler Based on MIV-ISAO-LSSVM

“…In many previous studies, researchers mainly used data analysis and other methods to carry out unit diagnosis, operation optimization, and other aspects of the work, and the accuracy of the research model will have a great impact on the effectiveness of the work. Currently, the primary prediction models consist of the neural network and support vector machine (SVM) [15]. The Least-Squares Support Vector Machine (LSSVM) model possesses the strengths of both, including the strong generalization ability and global optimization of SVM, while avoiding the issues of overfitting in neural networks and time-consuming training in SVM [16].…”