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

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.

Aiming at the difficulty of accurate prediction due to the randomness and nonstationary nature of blood glucose concentration series, a blood glucose concentration prediction model based on complementary ensemble empirical mode decomposition (CEEMD) and least squares support vector machine (LSSVM) is proposed. Firstly, CEEMD is used to convert the blood glucose concentration sequence into a series of intrinsic mode functions (IMFs) to reduce the impact of randomness and nonstationary signals on prediction performance. Then, a LSSVM prediction model is established for each mode IMF. The comprehensive learning particle swarm optimization (CLPSO) algorithm is used to optimize the kernel parameters of LSSVM. Finally, the prediction results of all IMFs are superimposed to yield the final blood glucose concentration prediction value. The experimental results show that the proposed prediction model has higher prediction accuracy in short-term blood glucose concentration values.

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

Spatiotemporal Characteristics of Precipitation and Drought Variability in Taiwan Using Multi-dimensional Complementary Ensemble Empirical Mode Decomposition AbstractMost time series observed in natural systems are nonlinear and nonstationary, particularly under the influence of climate change. Taiwan has experienced increasingly frequent drought events in recent decades. Droughts are characterized by their gradual development, cumulative impacts, and lack of clear early warning signals, which makes their detection and analysis challenging. To address these issues, this study applies Multi-dimensional Complementary Ensemble Empirical Mode Decomposition (MCEEMD) to analyze long-term temperature and precipitation data in Taiwan from 1960 to 2023. MCEEMD is an effective time–frequency analysis method designed for nonlinear and nonstationary time series. It enables the decomposition of multi-dimensional signals into a set of Intrinsic Mode Functions (IMFs), allowing both spatial and temporal characteristics of climate variables to be examined. Through these IMFs, meaningful instantaneous frequencies and long-term trends in the signals can be identified. This study considers both stochastic and deterministic influences by reconstructing the IMFs into two components based on their autocorrelation coefficients. The relationships between temperature, precipitation variability, and drought-related characteristics are then examined, providing insights into the spatiotemporal behavior of drought events in Taiwan. Key word: Multi-dimensional Complementary Ensemble Empirical Mode Decomposition(MCEEMD ),Intrinsic Mode Functions (IMFs)

Wind power prediction accuracy is closely related to the quality of wind power data. However, most of traditional prediction methods do not consider the cybersecurity of raw data. So, it would be important to study the prediction accuracy under false data injection attack (one type of cybersecurity attacks) and its influence on the economy and security of power system. In this paper, very short-term forecasting approach of wind power generation is studied for choosing the optimal prediction model. Different scenarios of false data injection attack are then simulated upon wind power data. Wind power prediction accuracy after being affected by false data injection is observed. The robustness of wind power prediction model is evaluated. Finally, wind power prediction information is applied to day-ahead unit commitment to evaluate the impact of cybersecurity of wind power forecasting on power system economy and security.

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.

In this paper, a combination model based on complementary ensemble empirical mode decomposition (CEEMD) is proposed. First, CEEMD is applied to decompose original network traffic to generate high‐frequency component, low‐frequency component, and residual component. Then, the high‐frequency components are modeled and predicted using bi‐directional long short‐term memory (BiLSTM). The low‐frequency components and the residual component are modeled and predicted using autoregressive integrated moving average (ARIMA). Meanwhile, considering that the BiLSTM model is influenced by the hyperparameters, an Improved Bald Eagle Search (IBES) algorithm is proposed and applied to optimize three hyperparameters of BiLSTM, avoiding the blindness and subjectivity of manual selection of parameters. Finally, the prediction values of BiLSTM and ARIMA model are summed to obtain the final predicted value of network traffic. The comparisons with other models proved that the proposed network traffic prediction model is closer to the real data, with the optimal performance indicators, which is very suitable for high precision occasions.

Accurate wind power prediction is of great significance for grid stability and renewable energy integration. Addressing the challenge of effectively integrating physical mechanisms with data-driven methods in wind power prediction, this paper innovatively proposes a two-stage deep learning prediction framework incorporating physics-guided fusion and distribution constraints, aiming to improve the prediction accuracy and physical authenticity of individual wind turbines. In the first stage, we construct a baseline model based on multi-branch multilayer perceptrons (MLP) that eschews traditional attempts to accurately reconstruct complex three-dimensional spatiotemporal wind fields, instead directly learning the power conversion characteristics of wind turbines under specific meteorological conditions from historical operational data, namely the power coefficient (Cp). This data-driven Cp fitting method provides a physically interpretable and robust benchmark for power prediction. In the second stage, targeting the prediction residuals from the baseline model, we design a bidirectional long short-term memory network (BiLSTM) for refined correction. The core innovation of this stage lies in introducing Maximum Mean Discrepancy (MMD) as a regularization term to constrain the predicted wind speed-power joint probability distribution. This constraint enforces the model-generated power predictions to remain statistically consistent with historical real data distributions, effectively preventing the model from producing predictions that deviate from physical reality, significantly enhancing the model’s generalization capability and reliability. Experimental results demonstrate that compared to traditional methods, the proposed method achieves significant improvements in Mean Absolute Error, Root Mean Square Error, and other metrics, validating the effectiveness of physical constraints in improving prediction accuracy.

Obtaining the instantaneous frequency of seismic data by Hilbert transform is widely used in reservoir prediction and fluid identification. In order to obtain high accuracy and clear physical significance instantaneous frequency, it is usually required that the analyzed signal is stationary and narrow-band. While practical seismic data is a nonstationary and bandwidth signal, directly applying Hilbert transform to seismic data to obtain instantaneous frequency will lack physical significance or even distort. Complementary Ensemble Empirical Mode Decomposition (CEEMD) can adaptively decompose a complex signal into a limited number of Intrinsic Mode Function (IMF), which is stationary, narrow-band and contains the local characteristics information of the original signal. Therefore, applying Hilbert transform to IMF can obtain high accuracy and clear physical significance instantaneous frequency. The amplitude and frequency attributes of seismic data will change abnormally after the reservoir is oil or gas bearing. Extracting the corresponding abnormal change can be used as an effective indicator to identify hydrocarbon information. In this article, the Hilbert Huang transform based on CEEMD is applied to hydrocarbon detection. First, the seismic data is processed by CEEMD to obtain IMF components. Then the IMF component which can reflect the local anomaly changes caused by hydrocarbon is selected. Finally, the instantaneous frequency of the IMF component related to hydrocarbon is obtained by Hilbert transform, and take it as the sign of hydrocarbon detection. Note: This paper was accepted into the Technical Program but was not presented at IMAGE 2021 in Denver, Colorado.

Rotating devices such as gears often generate fault vibration signals during eration. Under the interference of strong external noise, it is often difficult to extract these weak fault feature. To this end, a method based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Singular Spectrum Analysis (SSA) is proposed to reduce the noise of the gear signals. Firstly, original vibration signals are decomposed into a series of intrinsic mode functions (IMFs) by CEEMD, and the reconstructed signals are obtained after the some IMFs are removed according to evaluation coefficients. Then, the reconstructed signals are further denoised using SSA. After singular value decomposition, grouping and reconstruction, the denoised time series are obtained. The actual gear vibration signals from the QPZZ-II rotating machinery fault simulation rig are used to verify the effectiveness of the proposed method. The results of comparing with the different denoising methods show that the presented method can effectively depress the strong noise, and extract weak fault feature.

In recent years, due to the non-stationary behavior of data samples, modeling and forecasting the stock price has been challenging for the business community and researchers. In order to address these mentioned issues, enhanced machine learning algorithms can be employed to establish stock forecasting algorithms. Accordingly, introducing the idea of “decomposition and ensemble” and the theory of “granular computing”, a hybrid model in this paper is established by incorporating the complementary ensemble empirical mode decomposition (CEEMD), sample entropy (SE), independent component analysis (ICA), particle swarm optimization (PSO), and long short-term memory (LSTM). First, aiming at reducing the complexity of the original data of stock price, the CEEMD approach decomposes the data into different intrinsic mode functions (IMFs). To alleviate the cumulative error of IMFs, SE is performed to restructure the IMFs. Second, the ICA technique separates IMFs, describing the internal foundation structure. Finally, the LSTM model is adopted for forecasting the stock price results, in which the LSTM hyperparameters are optimized by synchronously utilizing the PSO algorithm. The experimental results on four stock prices from China stock market reveal the accuracy and robustness of the established model from the aspect of statistical efficiency measures. In theory, a useful attempt is made by integrating the idea of “granular computing” with “decomposition and ensemble” to construct the forecasting model of non-stationary data. In practice, the research results will provide scientific reference for the business community and researchers.

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

Accurate wind power prediction can effectively alleviate the pressure of the power system peak frequency regulation, and is more conducive to the economic dispatch of the power system. To enhance wind power forecasting accuracy, a hybrid approach for wind power interval prediction is proposes in this study. Firstly, an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) is applied to decompose the initial wind power sequence into multiple modes, and Variational Mode Decomposition is used to further decompose the high-frequency non-stationary components. Next, Fuzzy Entropy (FE) is utilized to assess the complexity of the post-decomposed Intrinsic Mode Functions (IMFs), and different forecasting methods are employed accordingly, the point predictions were obtained by linearly summing the component predictions.Additionally, an improved sparrow search algorithm (ISSA) is used to seek the optimal hyperparameters of the prediction algorithm. Finally, the prediction intervals are constructed using the point prediction results based on kernel density estimation (KDE). The root mean square errors (RMSE) of deterministic predictions are 2.8458 MW and 1.8605 MW, with uncertainty coverage rates of 95.83% and 97.92% at a 95% confidence level.

Accurately identifying faults in rolling bearing systems by analyzing vibration signals, which are often nonstationary, is challenging. To address this issue, a new approach based on complementary ensemble empirical mode decomposition (CEEMD) and time series modeling is proposed in this paper. This approach seeks to identify faults appearing in a rolling bearing system using proper autoregressive (AR) model established from the nonstationary vibration signal. First, vibration signals measured from a rolling bearing test system with different defect conditions are decomposed into a set of intrinsic mode functions (IMFs) by means of the CEEMD method. Second, vibration signals are filtered with calculated filtering parameters. Third, the IMF which is closely correlated to the filtered signal is selected according to the correlation coefficient between the filtered signal and each IMF, and then the AR model of the selected IMF is established. Subsequently, the AR model parameters are considered as the input feature vectors, and the hidden Markov model (HMM) is used to identify the fault pattern of a rolling bearing. Experimental study performed on a bearing test system has shown that the presented approach can accurately identify faults in rolling bearings.

Accurate wind power prediction can reduce the negative impact of wind power on power grid and reduce the operation cost of power system. Wind power forecasting is of great significance in spot trading, but the instability of wind power series brings difficulties to forecasting. With the rapid development of deep learning technology, prediction algorithm based on deep learning has been widely used in wind power prediction. The long short-term memory (LSTM) network performs well in a variety of prediction scenarios, but there is still room for improvement. In order to improve the prediction accuracy of LSTM in wind power prediction, this paper adopts a new gating mechanism in LSTM network, which can improve the context information modeling ability of LSTM network. The experimental results based on real data show that the proposed improved LSTM prediction model can improve the prediction accuracy of LSTM prediction model in ultra-short term wind power prediction scenarios.