An Ensemble Learning Model for Ultra‐Short‐Term Wind Power Prediction Based on Variational Mode Decomposition and Adaptive Weighting

With the rising demand for environmental protection, wind energy has made considerable progress as a clean energy source. Due to the uncertainty of wind power generation, accurate forecasting of wind power is conducive to achieving stable grid-connected control of wind turbines. This paper proposes an ultra-short-term wind power forecasting model composed of variational modal decomposition (VMD) and long short-term memory (LSTM) networks. Obtain the recorded historical wind speed and historical power from the wind field data acquisition and monitoring control system (SCADA), and use VMD to decompose and reconstruct the historical wind speed of the wind field to form 5 training data sets. Using historical power, historical wind speed, and predicted wind speed as input, wind power forecasts for the next 4 hours (15 minutes apart) are made. In order to evaluate the prediction ability of the proposed model, two algorithms, LSTM and XGBoost, are used for prediction. Taking root mean square error (RMSE) and mean absolute error (MAE) as evaluation indicators, the results show that the prediction accuracy of wind power is significantly improved after VMD processing, and the performance of LSTM is better than XGBoost in the two evaluation indicators.

An improved residual-based convolutional neural network for very short-term wind power forecasting

A new distributed decomposition–reconstruction–ensemble learning paradigm for short-term wind power prediction

A new intelligent method based on combination of VMD and ELM for short term wind power forecasting

Accurate wind power forecasting (WPF) is pivotal for the power system dominated by high penetration of renewable energy. Most forecasting techniques require sufficient data samples as a premise for achieving accurate prediction. Due to equipment faults during data collection, complete data is not always available, resulting that the forecasting accuracy is greatly diminished. To address this issue, this paper proposes a novel two‐stage hybrid forecasting approach including data restoration stage and forecasting stage. For the data restoration stage, the bidirectional long short‐term memory (Bi‐LSTM) is integrated into generative adversarial network (GAN) to recover the missing data with consideration of the complex time dynamics and correlations among heterogeneous data. To improve the prediction accuracy, the complete generated wind power sequence is decomposed into multiple time sequences with low volatility based on the enhanced variational mode decomposition (VMD). For the forecasting stage, a hybrid forecasting algorithm that combines convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) with an improved attention mechanism is proposed, strengthening the forecasting performance by assigning optimal weights to key features. The proposed hybrid forecasting method outperforms traditional methods based on real wind farm data with different shares of data loss from Guangxi province in China. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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

Energy storage scheduling design on friendly grid wind power

The uncertainty of wind power as the main obstacle of its integration into the power grid can be addressed by an accurate and efficient wind power forecast. Among the various wind power forecasting methods, machine learning (ML) algorithms, are recognized as a powerful wind power forecasting tool, however, their performance is highly dependent on the proper tuning of their hyperparameters. Common hyperparameter tuning methods such as grid search or random search are time-consuming, computationally expensive, and unreliable for complex models such as deep learning neural networks. Therefore, there is an urgent need for automatic methods to discover optimal hyperparameters for higher accuracy and efficiency of prediction models. In this study, a novel investigation is contributed to the field of wind power forecasting by a comprehensive comparison of three advanced techniques – Scikit-opt, Optuna, and Hyperopt – for hyperparameter optimization of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) models, a facet that, to our knowledge, has not been systematically explored in existing literature. The impact of these optimization techniques on the accuracy and efficiency of the CNN and LSTM models are assessed by comparing the root mean square error (RMSE) of the predictions and the required time to tune the models. The results show that the Optuna algorithm, using a Tree-structured Parzen Estimator (TPE) search method and Expected Improvement (EI) acquisition function, has the best efficiency for both CNN and LSTM models. In terms of accuracy, it is demonstrated that while for the CNN model all the optimization methods achieve similar performances, the LSTM model optimized by the Hyperopt algorithm, based on the annealing search method, results in the highest accuracy. In addition, for the first time in this research, the impact of the random initialization features on the performance of the forecasting models with neural networks is investigated. The proposed structures for deep learning models were examined to determine the most robust structure with the minimal sensitivity to the randomness. What we have discovered from the comparison of advanced hyperparameter optimization methods can be used by researchers to tune the time series-based forecasting models.

Accurate wind power forecasting (WPF) is crucial to enhance availability and reap the benefits of integration into power grids. The time lag of wind power generation lags the time of wind speed changes, especially in ultra-short-term forecasting. The prediction model is sensitive to outliers and sudden changes in input historical meteorological data, which may significantly affect the robustness of the WPF model. To address this issue, this paper proposes a novel hybrid machine learning model for highly accurate forecasting of wind power generation in ultra-short-term forecasting. The raw wind power data were filtered and classified with the local outlier factor (LOF) and the voting tree (VT) model to obtain a subset of inputs with the best relevance. The time-varying properties of the fluctuating sub-signals of the wind power sequences were analyzed with the optimized variational mode decomposition (OVMD) algorithm. The Northern Goshawk optimization (NGO) algorithm was improved by incorporating a logical chaotic initialization strategy and chaotic adaptive inertia weights. The improved NGO algorithm was used to optimize the least squares support vector regression (LSSVR) prediction model to improve the computational speed and prediction results. The proposed model was compared with traditional machine learning models, deep learning models, and other hybrid models. The experimental results show that the proposed model has an average R2 of 0.9998. The average MSE, average MAE, and average MAPE are as low as 0.0244, 0.1073, and 0.3587, which displayed the best results in ultra-short-term WPF.

The rapid expansion of wind power gives rise to a number of challenges for power system operators and electricity market participants. The key operational challenge is to efficiently handle the uncertainty and variability of wind power when balancing supply and demand in ths system. In this report, we analyze how wind power forecasting can serve as an efficient tool toward this end. We discuss the current status of wind power forecasting in U.S. electricity markets and develop several methodologies and modeling tools for the use of wind power forecasting in operational decisions, from the perspectives of the system operator as well as the wind power producer. In particular, we focus on the use of probabilistic forecasts in operational decisions. Driven by increasing prices for fossil fuels and concerns about greenhouse gas (GHG) emissions, wind power, as a renewable and clean source of energy, is rapidly being introduced into the existing electricity supply portfolio in many parts of the world. The U.S. Department of Energy (DOE) has analyzed a scenario in which wind power meets 20% of the U.S. electricity demand by 2030, which means that the U.S. wind power capacity would have to reach more than 300 gigawatts (GW). The European Union is pursuing a target of 20/20/20, which aims to reduce greenhouse gas (GHG) emissions by 20%, increase the amount of renewable energy to 20% of the energy supply, and improve energy efficiency by 20% by 2020 as compared to 1990. Meanwhile, China is the leading country in terms of installed wind capacity, and had 45 GW of installed wind power capacity out of about 200 GW on a global level at the end of 2010. The rapid increase in the penetration of wind power into power systems introduces more variability and uncertainty in the electricity generation portfolio, and these factors are the key challenges when it comes to integrating wind power into the electric power grid. Wind power forecasting (WPF) is an important tool to help efficiently address this challenge, and significant efforts have been invested in developing more accurate wind power forecasts. In this report, we document our work on the use of wind power forecasting in operational decisions.

Wind power serves as a clean and sustainable form of energy. However, its generation is fraught with variability and uncertainty, owing to the stochastic and dynamic characteristics of wind. Accurate forecasting of wind power is indispensable for the efficient planning, operation, and grid integration of wind energy systems. In this paper, we introduce a novel forecasting method termed Temporal Collaborative Attention (TCOAT). This data-driven approach is designed to capture both temporal and spatial dependencies in wind power generation data, as well as discern long-term and short-term patterns. Utilizing attention mechanisms, TCOAT dynamically adjusts the weights of each input variable and time step based on their contextual relevance for forecasting. Furthermore, the method employs collaborative attention units to assimilate directional and global information from the input data. It also explicitly models the interactions and correlations among different variables or time steps through the use of self-attention and cross-attention mechanisms. To integrate long-term and short-term information effectively, TCOAT incorporates a temporal fusion layer that employs concatenation and mapping operations, along with hierarchical feature extraction and aggregation. We validate the efficacy of TCOAT through extensive experiments on a real-world wind power generation dataset from Greece and compare its performance against twenty-two state-of-the-art methods. Experimental results demonstrate that TCOAT outperforms existing methods in terms of both accuracy and robustness in wind power forecasting. Moreover, we conduct a generality study on an additional real-world dataset from a different climate condition and wind power characteristics. The results show that TCOAT can achieve comparable or better performance than the state-of-the-art methods, confirming the generalization ability of TCOAT.

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.

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.

Spatial correlation learning based on graph neural network for medium-term wind power forecasting

A short-term wind power prediction model based on BiLSTM–CNN–WGAN-GP (LCWGAN-GP) is proposed in this paper, aiming at the problems of instability and low prediction accuracy of short-term wind power prediction. Firstly, the original wind energy data are decomposed into subsequences of natural mode functions with different frequencies by using the variational mode decomposition (VMD) algorithm. The VMD algorithm relies on a decision support system for the decomposition of the data into natural mode functions. Once the decomposition is performed, the nonlinear and dynamic behavior are extracted from each natural mode function. Next, the BiLSTM network is chosen as the generation model of the generative adversarial network (WGAN-GP) to obtain the data distribution characteristics of wind power’s output. The convolutional neural network (CNN) is chosen as the discrimination model, and the semi-supervised regression layer is utilized to design the discrimination model to predict wind power. The minimum–maximum game is formed by the BiLSTM and CNN network models to improve the quality of sample generation and further improve the prediction accuracy. Finally, the actual data of a wind farm in Jiuquan City, Gansu Province, China is taken as an example to prove that the proposed method has superior performance compared with other prediction algorithms.