Enhancing short-term wind power forecasting accuracy through a hybrid model of virtual prediction and wavelet packet transform

BiLSTM-InceptionV3-Transformer-fully-connected model for short-term wind power forecasting

The accuracy of short-term wind power forecast is important for the power system operation. Based on the real-time wind power data, a wind power prediction model using wavelet neural network (WNN) is proposed. In order to overcome such disadvantages of WNN as easily falling into local minimum, this paper put forward using Particle Swarm Optimization (PSO) algorithm to optimize the weight and threshold of WNN. It’s advisable to use Support Vector Machine (SVM) to comparatively do prediction and put two outcomes as input vector for Generalized Regression Neural Network (GRNN) to do nonlinear combination forecasting. Simulation shows that combination prediction model can improve the accuracy of the short-term wind power prediction.

The intermittency, uncontrollability, and variability of wind power affect the economical operation and reliable delivery of the power system. To ensure the smooth integration of wind power into the grid, an accurate wind power forecast is essential. In this paper, we propose a short-term wind power prediction model based on decomposition and reconstruction to improve the accuracy of wind power forecasts. This model has the following characteristics: (i) wind power time series are decomposition into multiple components through the application of a decomposition method that combines fully integrated empirical mode decomposition with adaptive noise algorithm; (ii) the components are clustered using K-means clustering based on dynamic time warping. According to the similarity of the complexity and lag of components, they are divided into three classes; (iii) three different prediction models, including seasonal autoregressive integrated moving average model (SARIMA), long short-term memory network (LSTM) and Bi-directional long short-term memory network (BiLSTM), predict three types of components respectively. Finally, to illustrate the capability of the model, we compare its performance with three typical models. The results demonstrate that the proposed method exceeds the baseline models in regard to prediction performance.

Short-term wind power prediction based on multidimensional data cleaning and feature reconfiguration

Multistep short-term wind power forecasting model based on secondary decomposition, the kernel principal component analysis, an enhanced arithmetic optimization algorithm, and error correction

Wind power generation has the characteristics of intermittency and uncertainty. It is significant to predict intervals of short-term wind power precisely for optimizing the grid power system operation scheduling and reserve capacity. The paper proposed a simple short-term wind power intervals prediction model based on artificial bee colony-neural network (ABC-NN). A new criterion was developed in this method to get better prediction results. Then generalized morphological filter was applied in the de-noising of wind power time sequence by analyzing of wind power constitute components. The simulation results demonstrate that the new method has a better performance in wind power intervals prediction.

A new short-term wind power prediction methodology based on linear and nonlinear hybrid models

Using enhanced crow search algorithm optimization-extreme learning machine model to forecast short-term wind power

Accurate short-term solar and wind power predictions play an important role in the planning and operation of power systems. However, the short-term power prediction of renewable energy has always been considered a complex regression problem, owing to the fluctuation and intermittence of output powers and the law of dynamic change with time due to local weather conditions, i.e. spatio-temporal correlation. To capture the spatio-temporal features simultaneously, this paper proposes a new graph neural network-based short-term power forecasting approach, which combines the graph convolutional network (GCN) and long short-term memory (LSTM). Specifically, the GCN is employed to learn complex spatial correlations between adjacent renewable energies, and the LSTM is used to learn dynamic changes of power generation curves. The simulation results show that the proposed hybrid approach can model the spatio-temporal correlation of renewable energies, and its performance outperforms popular baselines on real-world datasets.

Seeking green and low-carbon development has become a common consensus of the international community, and wind energy is one of the best new energy sources for the development of low-carbon economy. However, wind power has strong randomness and volatility, which will bring new challenges to the green and low-carbon operation of new power systems. Relying on the new power system, accurate power prediction of wind turbines can not only optimize the power supply structure of the power system, but also promote energy conservation, emission reduction and ‘low-carbon’ development of the power industry. Therefore, under the above background, this paper proposes a short-term wind power prediction model based on Singular Spectrum Analysis-Temporal Convolutional Network-Support Vector Machine (SSA-TCN-SVM). Aiming at the problem that the Temporal Convolutional Network (TCN) prediction model still has complex modes and the Dense Layer cannot set the weights reasonably, a cascade model based on Temporal Convolutional Network-Support Vector Machine (TCN-SVM) is adopted. The kernel function of Support Vector Machine (SVM) is used to map the vectors output by the convolution kernel channel of each time to the high-dimensional kernel space. The optimal weights are obtained by solving the dual problem, and the prediction ability of the single grouping sequence prediction model is improved. By minimizing the prediction error of the grouping sequence, the prediction accuracy of the whole prediction model is improved. Experiments show that SSA-TCN-SVM has better prediction performance.

Accurate and stable wind power forecasting is an inevitable requirement for efficient use of renewable energy. This paper proposes a short-term wind power combination forecasting method based on mode decomposition and feature extraction. The original wind speed time series is decomposed into multiple subsequences by the ensemble empirical mode decomposition (EEMD). The generalized mutual information (GMI) is used to extract the optimal input feature set of each subsequence. The prediction values of each subsequence are obtained based on the least squares support vector machine (LSSVM) model, and then the final prediction results are obtained by combining them. Finally, the paper proves that the proposed method can predict short-term wind power more accurately and stably by setting up the contrast experiment.

Reliable wind power forecasting is essential for maintaining the safe and stable operation of power systems with high renewable energy penetration. This study proposes a short-term wind power forecasting model based on decomposition–optimization–prediction, integrating complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the improved football team training algorithm (IFTTA), and the bidirectional long short-term memory network model (BiLSTM). CEEMDAN is employed to decompose the non-stationary wind power sequence into relatively stable intrinsic mode functions (IMFs), thereby separating multi-scale fluctuation features. The IFTTA incorporates a dynamic probability allocation strategy and an adaptive parameter adjustment mechanism, which contributes to a better balance between global exploration and local exploitation. After optimizing the hyperparameters of BiLSTM using IFTTA, the prediction performance significantly improved. Validations were conducted on three datasets from Xinjiang, Ningxia, and Inner Mongolia, China, each containing 1440 samples (1152 for training and 288 for testing). Comparisons with the benchmark forecasting model demonstrate that the pro-posed model reduces the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) by at least 25.29%, 29.62%, and 20.66%, respectively. Correspondingly, the coefficient of determination (R2) was improved by at least 0.0069. This model provides an effective solution for short-term wind power prediction in practical engineering.

In the traditional weather classification prediction method based on clustering, the feature quantity is difficult to fully reflect the change information of complex weather, which will weaken the reliability of classification. Therefore, this paper proposes a wind power prediction model based on meteorological process division. Firstly, this method uses the correlation coefficient to analyze the correlation between various meteorological factors and wind power output in numerical weather and then uses the wind speed after wavelet denoising to divide the weather fluctuation process and extract the fluctuation characteristics of each fluctuation process. Secondly, the meteorological process is divided by a hierarchical clustering algorithm, and the types of meteorological processes are judged by a dynamic time planning algorithm. Finally, the wind power prediction model of a long short-term memory (LSTM) network is established based on different meteorological processes. The results show that this method can provide a reference for improving the accuracy and interpretability of short-term wind power forecasting.

<p>This research accounts for the outcome of a machine learning algorithm project to predict short term solar and wind power in Ontario. A Long Short Term Memory (LSTM) model was developed to monitor short term power output predictions from 4 hours ahead to 6 hours ahead. This study demonstrates a unique approach of utilizing nearby publicly available meteorological data to predict renewable energy power output that could potentially be used for wind and solar farm scheduling to prevent curtailment of clean energy. The wind power and the solar power was predicted 6 and 4 hours ahead with a coefficient of correlation (R2) of 0.817 and 0.738 respectively. It has demonstrated the usage of a LSTM network as a reliable tool for the prediction of renewable energy that can be implemented to power output on systems that require accurate prediction of wind and solar power within the 4 to 6 hours. </p>

An accurate forecast of wind power plays an increasingly important role in economic dispatch and power system operations. Nevertheless, the randomness, intermittence, and volatility of wind power pose great challenges in power system balance. To improve the forecasting accuracy of wind power, a multi-scale fusing deep learning method is proposed to forecast short-term wind power for multiple wind turbine generators in a wind farm. The inherent spatio-temporal characteristics are studied by dividing the time scale into a small-scale nearby part and a large-scale historical part. The fine-grained dynamic characteristics in the small-scale nearby part are extracted using stacked three-dimensional convolutional neural networks (3D-CNN), and the steady-state characteristics in the large-scale historical part are extracted using Spatio-Temporal Long Short-Term Memory (ST-LSTM) units. Then, wind power for multiple wind turbine generators can be forecasted by fusing the two deep learning channels. An actually collected wind power dataset is used to validate the effectiveness of the proposed method, which is exported from 33 wind turbine generators in a wind farm located in a northwestern province of China. The experimental results show that the proposed wind power forecasting method can improve the forecasting accuracy effectively when compared with the state-of-the-art wind power forecasting methods.