Short-term Wind Power Forecasting Research Articles

Hybrid model for short-term wind power forecasting based on singular spectrum analysis and a temporal convolutional attention network with an adaptive receptive field

Accurate and robust short-term wind power forecasting (WPF) is of great significance to enhance the rate of renewable energy utilization in power systems and to promote low-carbon energy transformation. However, the high randomness and complex volatility of wind power bring great challenges when designing reliable and accurate forecasting models. In this paper, a novel hybrid model based on singular spectrum analysis (SSA) and a temporal convolutional attention network with an adaptive receptive field (ARFTCAN) is proposed. Specifically, to ensure the sufficiency and completeness of feature decomposition and reconstruction, we develop an SSA-based component partitioning mechanism to decompose complex original wind power sequences and determine their trend, period and noise components. Moreover, a self-attention mechanism and the adaptive receptive field (ARF) algorithm are integrated into a temporal convolutional network (TCN) to ensure the automatic extraction of multiple critical frequency-domain features within the complete fluctuation period. Furthermore, the forecasting results obtained with different feature components are integrated into the final model to realize identification, reconstruction and extrapolation from a multifrequency-domain perspective. The results demonstrate that the proposed model effectively supports the adaptability of short-term WPF in four seasons. Especially in scenarios with high-frequency wind power fluctuations, the mean absolute percentage error (MAPE) of the proposed model is reduced by more than 52% relative to those of the state-of-the-art decomposition-forecasting models. Moreover, compared to the classic SSA-based deep learning models, the proposed model achieves an MAPE reduction of over 13% in a scenario with low power output.

Short-term regional wind power forecasting based on spatial–temporal correlation and dynamic clustering model

Short-term regional wind power forecasting (WPF) is essential for enhancing the power grid’s robustness. In the field of regional short-term power forecasting, numerical weather forecasts (NWP) and regional wind-power historical data have been commonly used in power forecasting. In this paper, a regional wind power prediction model based on the spatial and temporal correlation of meteorological resources is proposed to predict the wind power in the next seven days. First, combining variational mode decomposition and Granger causality test (VMD-GCT) to analyze the NWP-related meteorological factors to the wind power components in different bands to obtain the screening of the NWP-related factors. Then, the correlation analysis technique, which uses the physical characteristics of the wind speed time lag in a region, dynamically analyzes the spatial correlation time lag between a wind farm in the region and other wind farms, and uses the DBSCAN (density-based spatial clustering of applications with noise) algorithm to dynamically divide wind power clusters in the wind power forecast cycle, thus laying the foundation for the input data of the prediction model. Finally, the short-term power forecasting in the region is performed by a combined deep learning model. The results show that the proposed method can significantly improve the accuracy and efficiency of the wind power prediction for clusters at different wind speeds. The proposed method is of great significance for the short-term prediction of wind power in a region.

An Improved Long Short-Term Memory Neural Network Wind Power Forecast Algorithm Based on TEMD Noise Reduction

To accurately predict the wind power and adopt methods to balance the fluctuation of power grid, an improved long short-term memory (LSTM) neural network wind power forecast algorithm based on noise reduction by threshold empirical modal decomposition (TEMD) is proposed. First, the actual operation and maintenance data of wind farms are normalized and divided into a training set and a test set. Then, an LSTM structure is designed and a Sub-Grid Search (SGS) algorithm is proposed to optimize the hyperparameters of the LSTM network. Finally, the power data are decomposed and noise-reduced using TEMD is improved by the variable-point technique and the TEMD-LSTM power forecast model is constructed to predict the power in time. The predicted values obtained are restored and evaluated by the original size. The results show that compared with five other algorithms of the same kind, the proposed algorithm in this paper has a root mean square error (RMSE) of 30.40, a trend accuracy (TA) value of 67.23% and a training time of 886 s, with the best overall performance.

Short-term wind power prediction with harmony search algorithm: Belen region

Wind power is the fastest-growing technology among alternative energy production sources. Reliable forecasting of short-term wind power plays a critical role in the acquisition of most of the generated energy. In this study, short-term wind power forecast is performed using radial-based artificial neural networks, forecast error and cost to be minimized with the harmony search algorithm. Experimented results show that, we can predict wind power with fewer features and less error by using harmony search algorithm. A %7 percent improvement in RMSE rate has been achieved with the proposed method for short-term wind power prediction.

Short-term prediction of the power of a new wind turbine based on IAO-LSTM

Short-term wind power forecasting is of great significance to the real-time dispatching of power systems, but the short-term forecasting accuracy of wind power is not high. To this end, this paper proposes a hybrid prediction model that combines the Isolated Forest algorithm, the Synchronous Squeeze Wavelet Transform (SWT) method, the Aquila Optimizer (AO) and the Long Short-term Memory network (LSTM). Firstly, the Isolated Forest algorithm is used to detect abnormal data. Secondly, the SWT method is used to denoise the original power signal of the new wind turbine. Then, the wind power prediction model is established through the long short-term memory network algorithm. The OA is used to optimize the LSTM structure parameters to solve the influence of random parameters on the prediction accuracy. Finally, perform example verification. The results show that the proposed model is effective in power prediction of new wind turbine.

A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power Forecasting

Short-term wind power forecasting (SWPF) is essential for managing wind power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate multi-view learning technologies with attention mechanisms. In this case, some potential features cannot be fully extracted, degenerating the predictive accuracy and robustness in SWPF. To solve this problem, this paper proposes a multi-view ensemble width-depth neural network (MVEW-DNN) for SWPF. Specifically, MVEW-DNN consists of local and global view learning subnetworks, which can effectively achieve more potential global and local view features of the original wind power data. In MVEW-DNN, the local view learning subnetwork is developed by introducing the deep belief network (DBN) model, which can efficiently extract the local view features. On the other hand, by introducing the attention mechanism, a new deep encoder board learning system (deBLS) is developed as the global view learning subnetwork, which provides more comprehensive global information. Therefore, by rationally learning the effective local and global view features, MVEW-DNN can achieve competitive predictive performance in SWPF. MVEW-DNN is compared with the state-of-the-art models in SWPF. The experiment results indicate that MVEW-DNN can provide competitive predictive accuracy and robustness.

Short-Term Wind Power Prediction via Spatial Temporal Analysis and Deep Residual Networks

Wind power is a rapidly growing source of clean energy. Accurate short-term forecasting of wind power is essential for reliable energy generation. In this study, we propose a novel wind power forecasting approach using spatiotemporal analysis to enhance forecasting performance. First, the wind power time-series data from the target turbine and adjacent neighboring turbines were utilized to form a graph structure using graph neural networks (GNN). The graph structure was used to compute the spatiotemporal correlation between the target turbine and adjacent turbines. Then, the prediction models were trained using a deep residual network (DRN) for short-term wind power prediction. Considering the wind speed, the historic wind power, air density, and historic wind power in adjacent wind turbines within the supervisory control and data acquisition (SCADA) system were utilized. A comparative analysis was performed using conventional machine-learning approaches. Industrial data collected from Hami County, Xinjiang, China, were used for the case study. The computational results validate the superiority of the proposed approach for short-term wind-power forecasting.

Turbine-level clustering for improved short-term wind power forecasting

At the present time, new types of data are collected at a turbine level, and can be used to enhance the skill of short-term wind power forecasts. In particular, high resolution measurements such as wind power and wind speed are gathered using SCADA systems. These data can be used to build turbine-tailored forecasting models, but at a higher computational cost to predict the production of the overall wind farm compared to a single farm-level model. Thus, we explore the potential of the DBSCAN clustering algorithm to group wind turbines and build forecasting models at a cluster-level to find a middle ground between forecasting accuracy and computational cost. The proposed approach is evaluated using SCADA data collected in two Irish wind farms.

Statistical downscaling of numerical weather prediction based on convolutional neural networks

Numerical Weather Prediction (NWP) is a necessary input for short-term wind power forecasting. Existing NWP models are all based on purely physical models. This requires mainframe computers to perform large-scale numerical calculations and the technical threshold of the assimilation process is high. There is a need to further improve the timeliness and accuracy of the assimilation process. In order to solve the above problems, NWP method based on artificial intelligence is proposed in this paper. It uses a convolutional neural network algorithm and a downscaling model from the global background field to establish a given wind turbine hub height position. We considered the actual data of a wind farm in north China as an example to analyze the calculation example. The results show that the prediction accuracy of the proposed method is equivalent to that of the traditional purely physical model. The prediction accuracy in some months is better than that of the purely physical model, and the calculation efficiency is considerably improved. The validity and advantages of the proposed method are verified from the results, and the traditional NWP method is replaced to a certain extent.

Combined Approach for Short-Term Wind Power Forecasting Based on Wave Division and Seq2Seq Model Using Deep Learning

The accuracy of short-term wind power forecasting (WPF) can be improved by effective mining of numerical weather prediction data. In this article, a novel short-term WPF approach is proposed by combining wave division (WD), improved grey wolf optimizer based on fuzzy C-means clusters (IGFCM), and Seq2Seq model with attention mechanism based on long short-term memory model (LSTMS), named the WD-IGFCM-LSTMS model. Based on the fluctuation trend, the wind speed sequences of NWP are divided into a series of waves. Six fluctuation features that reflect the shape characteristics are extracted to quantify the partitioned waves. A new strategy is proposed to improve the global searching ability of the GWO to select the initial clustering center of FCM more effectively. The Seq2Seq deep learning model based on LSTM, named LSTMS, is applied for wave-oriented forecasting. The proposed approach outperforms the traditional point-to-point forecasting and realizes continuous sequence forecasting. The simulation results demonstrate that the WD-IGFCM-LSTMS model can perform better than other benchmark forecasting models.

Joint modelling wind speed and power via Bayesian Dynamical models

The relationship of dependence between wind speed and wind power variables has a degree of complexity that has motivated several scientific studies over the years. Much of this research seeks to understand the stochastic nature of both phenomena, either for the purpose of marginal analysis or for joint analyses, aiming to improve prediction of wind power. The present study proposes three dynamic Bayesian models for wind energy that account for the complexity of both variables via hierarchical structures such as temporal dependence, nonstationary behaviour, and truncation of power due to turbine specifications. This hierarchy considers wind energy modelling conditioned on wind speed and a marginal model for wind speed. This approach allows joint analysis to be carried out via univariate time series modelling. Analysis of a rich dataset from Bahia state (Brazil) indicates that the proposed models are accurate for both short-term and long-term wind power forecasts.

Numerical Weather Prediction Correction Strategy for Short-Term Wind Power Forecasting Based on Bidirectional Gated Recurrent Unit and XGBoost

Accurate short-term wind power forecasting (WPF) plays a crucial role in grid scheduling and wind power accommodation. Numerical weather prediction (NWP) wind speed is the fundamental data for short-term WPF. At present, reducing NWP wind speed forecast errors contributes to improving the accuracy of WPF from the perspective of data quality. In this article, a variational mode decomposition combined with bidirectional gated recurrent unit (VMD-BGRU) method for NWP wind speed correction and XGBoost forecasting model are proposed. First, several NWP wind speed sub-series are divided by VMD to obtain more abundant multidimensional timing features. BGRU is applied to establish the potential relation between decomposed NWP wind speed sub-series and measured wind speed and get the proposed wind speed correction model. Then, a more clear regression forecasting model is trained based on XGBoost using historical measured wind speed and power. The corrected NWP wind speed is used to forecast wind power by XGBoost. Finally, the superiority of the proposed method is validated on a wind farm located in China. The results show that the proposed correction model and forecasting model outperform other compared models.

Short-term wind power forecast based on chaotic analysis and multivariate phase space reconstruction

The randomness and volatility of wind power time series, which are an external reflection of their internal chaotic dynamics, have always been important factors affecting the accuracy of wind power prediction. The chaotic characteristics of wind power data have not been studied deeply enough in existing research. Therefore, in this paper, a short-term wind power forecast method based on chaotic analysis is proposed, including chaotic time series estimation and multivariate phase space reconstruction (PSR). First, we calculate the largest Lyapunov exponent of wind power time series to measure the degree of chaos in wind power data. Then, the chaotic characteristics of several wind power time series from some neighboring wind farms are analyzed, based on which two multivariate multi-dimensional PSR models are established. Afterwards, the nearest neighbors (NNs) of the data to be predicted are selected in the phase space and delivered to the least-square support vector machine (LSSVM) model to experiment the forecasting. Wind power data collected from several adjacent wind sites are used to conduct the simulation studies, and the results have shown the effectiveness of the proposed method and its advantage over the classic LSSVM model in terms of accuracy and stability.

A Wind Speed Correction Method Based on Modified Hidden Markov Model for Enhancing Wind Power Forecast

Short-term wind power forecast (WPF) depends highly on the wind speed forecast (WSF), which is the prime contributor to the forecasting error. To achieve more accurate WPF results, this article proposes a wind speed correction method to improve the WSF result obtained by using the weather research and forecasting (WRF) model. First, the WRF model is constructed to forecast the wind speed, and its performance is analyzed. Second, a novel hidden Markov model (HMM) is developed to explore both the temporal autocorrelation of WSF error and the nonlinear correlation between the WSF result and the error. In the model, the fuzzy C-means cluster is introduced to properly divide the hidden state space of HMM and the emission probability of HMM is improved as continuous by the kernel density estimation (KDE) to make full use of the observation information. The proposed HMM model is better at wind speed correction through modification. Third, the HMM is solved by the Viterbi algorithm and the minimum mean-square error regulation to correct the predicted wind speed. Finally, the deterministic and probabilistic WPF results are obtained by using another KDE model, the proposed method is demonstrated to be superior to the benchmarks in case studies.

Short-Term Wind Power Forecasting and Uncertainty Analysis Based on Fcm–Woa–Elm–Gmm

Short-Term Wind Power Forecasting and Uncertainty Analysis Based on Fcm–Woa–Elm–Gmm

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of theWind Power and Photovoltaic Energy

As wind and photovoltaic energy become more prevalent, the optimization of power systems is becoming increasingly crucial. The current state of research in renewable generation and power forecasting technology, such as wind and photovoltaic power (PV), is described in this paper, with a focus on the ensemble sequential LSTMs approach with optimized hidden-layers topology for short-term multivariable wind power forecasting. The methods for forecasting wind power and PV production. The physical model, statistical learning method, and machine learning approaches based on historical data are all evaluated for the forecasting of wind power and PV production. Moreover, the experiments demonstrated that cloud map identification has a significant impact on PV generation. With a focus on the impact of photovoltaic and wind power generation systems on power grid operation and its causes, this paper summarizes the classification of wind power and PV generation systems, as well as the benefits and drawbacks of PV systems and wind power forecasting methods based on various typologies and analysis methods.

Inferential Statistics and Machine Learning Models for Short-Term Wind Power Forecasting

The inherent randomness, intermittence and volatility of wind power generation compromise the quality of the wind power system, resulting in uncertainty in the system's optimal scheduling. As a result, it's critical to improve power quality and assure real-time power grid scheduling and grid-connected wind farm operation. Inferred statistics are utilized in this research to infer general features based on the selected information, confirming that there are differences between two forecasting categories: Forecast Category 1 (0–11 h ahead) and Forecast Category 2 (12–23 h ahead). In z-tests, the null hypothesis provides the corresponding quantitative findings. To verify the final performance of the prediction findings, five benchmark methodologies are used: Persistence model, LMNN (Multilayer Perceptron with LM learning methods), NARX (Nonlinear autoregressive exogenous neural network model), LMRNN (RNNs with LM training methods) and LSTM (Long short-term memory neural network). Experiments using a real dataset show that the LSTM network has the highest forecasting accuracy when compared to other benchmark approaches including persistence model, LMNN, NARX network, and LMRNN, and the 23-steps forecasting accuracy has improved by 19.61%.

A combined model for short-term wind power forecasting based on the analysis of numerical weather prediction data

Due to the fluctuation and intermittency of wind power resources, large-scale wind power integration brings serious challenges to power systems. Among the existing short-term forecasting methods, the accuracy and forecast period length can hardly meet the demand of power system economic dispatching and day-ahead power purchase markets. To further enhance the accuracy and increase the time scale, a short-term wind power forecasting (WPF) combined model based on numerical weather prediction (NWP) analysis is presented in this paper. First, according to the criterion of the minimum redundancy maximum relevance (mRMR) algorithm, several factors are sifted from the NWP multivariate data. Second, different characteristics of the factors are extracted, and weather patterns are divided into different types on the basis of these characteristics. Third, two deep learning models, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), are applied for short-term WPF respectively under different weather types. Furthermore, the forecast results of the two models are combined using the Induced Ordered Weighted Average (IOWA) operator. The actual data collected from a wind farm in Northwest China are used to verify the conclusions. Results show that the proposed method can forecast wind power under different weather circumstances and outperform existing Radial Basis Function (RBF), Extreme Learning Machine (ELM) and Support Vector Machine (SVM)-based methods with respect to forecasting accuracy.

Short-term offshore wind power forecasting - A hybrid model based on Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and deep-learning-based Long Short-Term Memory (LSTM)

Short-term time series wind power predictions are extremely essential for accurate and efficient offshore wind energy evaluation and, in turn, benefit large wind farm operation and maintenance (O&M). However, it is still a challenging task due to the intermittent nature of offshore wind, which significantly increases difficulties in wind power forecasting. In this paper, a novel hybrid model, using unique strengths of Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Deep-learning-based Long Short-Term Memory (LSTM), was proposed to handle different components in the power time series of an offshore wind turbine in Scotland, where neither the approximation nor the detail was considered as purely nonlinear or linear. Besides, an integrated pre-processing method, incorporating Isolation Forest (IF), resampling, and interpolation was applied for the raw Supervisory Control and Data Acquisition (SCADA) datasets. The proposed DWT-SARIMA-LSTM model provided the highest accuracy among all the observed tests, indicating it could efficiently capture complex times series patterns from offshore wind power.

A new wind power forecasting algorithm based on long short‐term memory neural network

Wind power is one of the most large-scale new energy sources. But wind power instability will affect power grid safety, which is in a great need for wind power forecasting algorithms. To accurately predict wind power and reduce power grid fluctuations, it proposes a new wind power forecasting (WPF) algorithm based on long short-term memory (LSTM) neural network using wind farm real operation data. First, the wind farm power data are de-averaged and divided into two different sets in order to meet the requirements of the algorithm. Then, structure of the LSTM neural network is designed and hyper-parameters are adjusted to improve accuracy of forecasting. Finally, the definite LSTM neural network is used for forecasting the power data in time series to derive the power forecasting value, which is reduced and evaluated according to the original size. The results show that compared with other forecasting methods, in short-term WPF at different time scales, this algorithm has smaller errors in power forecasting results, which is also suitable for long-term WPF.