A fast and efficient short-term wind speed forecast model for wind turbines

A new two-stage decomposition and integrated hybrid model for short-term wind speed prediction

Research on short-term wind speed prediction based on deep learning model in multi-fan scenario of distributed generation

Accurate prediction of wind speed is a prerequisite for the safe and accurate operation of wind power generation, however, WRF models typically do not produce sufficiently accurate wind speed predictions. This study proposed a Seasonal and Temporal Correlation - Deep Forest (STC-DF) model for offshore wind speed prediction. Different from traditional methods, the STC-DF model takes the advantages of the deep forest algorithm to automatically learn complex feature interactions without manual feature engineering. The model is designed to capture the seasonal and temporal characteristics of wind speed variations. To test the effectiveness of the proposed method, we applied the trained STC-DF model to an offshore wind farm in Hainan Province, China. Seven days of data from each season were selected for testing. The results show that the STC-DF model can effectively reduce the error caused by WRF forecast. The error index of the corrected wind speed reduced more than 40%, the accuracy of wind speed forecast increased 15%. And the method passed the multi-model comparison test and robustness experiment. These research results show that the STC-DF model has strong versatility and good correction ability, and is suitable for wind speed forecasting in different regions, which is a feasible method to improve the reliability of offshore wind power generation.

• The hourly wind speed prediction is an important problem. • A feed-forward (FF) multi-layer perceptron (MLP) artificial neural network (ANN) is used for prediction. • The FF MLP ANN is optimized by 9 state-of-art metaheuristic algorithms. • In the experiments, 38 years of hourly wind data belonging to 5 cities were used. • Grey Wolf Optimizer produced best results in terms of mean square error (MSE). The growing population has tremendously increased the daily energy demand all around the world. India is the second-most crowded nation in the world with approximately 1.4 billion people. New and renewable energy is on the agenda of India and in 2021 India possesses the fourth-largest installed capacity of wind power. Accurate prediction of wind speed is vital in wind farm design and operation. In this work, an hourly wind speed prediction with an artificial neural network optimized by a metaheuristics approach is conducted. A feed-forward (FF) multi-layer perceptron (MLP) artificial neural network (ANN) is used for the prediction of the hourly wind speed. In this study, 38 years of hourly wind data belonging to 5 cities (Ambur, Hosur, Kumbakonam, Nagapattinam, and Pudukottai) were used. These cities have different specific properties such as latitude, longitude, and altitude. The FF MLP ANN is optimized by 9 state-of-art metaheuristic algorithms. In this work, Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Biogeography Based Optimization (BBO), Evolutionary Strategy (ES), Genetic Algorithm (GA), Grey-Wolf-Optimizer (GWO), Population-Based Incremental Learning (PBIL), Particle Swarm Optimization (PSO), Tree-Seed Algorithm (TSA) have been used to optimize the weights of the ANN. GWO outperforms other metaheuristic algorithms in the prediction of wind speed with a FF MLP ANN model, with a success percentage rate of approximately 3% to 10,000%.

With an estimated offshore potential wind energy capacity of 575 gigawatts, the Great Lakes region is a promising area for future wind energy development. Electric utilities that use wind energy rely on accurate day-ahead wind energy forecasts, mainly informed by predicted wind speed, to account for the variability of wind energy production. Since wind turbines have not yet been developed in the Great Lakes, observational wind data is collected at very few sites. Hence, utilities may seek to use synthetic wind data to pre-train accurate day-ahead wind speed prediction models. Yet, prior studies have only utilized synthetic data in predicting sub-hourly winds speeds. We hypothesized that training long short-term memory neural networks to predict day-ahead wind speeds on synthetic wind data instead of observational wind data would increase accuracy since synthetic wind data is available over longer time spans than observational wind data. We used observational data from the Lake Michigan Wind Assessment and synthetic data from the National Offshore Wind Great Lakes dataset and found that networks trained on synthetic data had a lower mean absolute percentage error score in day-ahead wind speed prediction than networks trained on observational data. We also optimized additional parameters of the networks for both synthetic and observational network types, further improving accuracy. The availability of a wind speed prediction model trained on synthetic data will reduce reliance on historical observational data at future sites of wind energy infrastructure, allowing utilities to swiftly adapt accurate prediction methods to new sites.

Accurate and stable wind speed prediction is crucial for the safe operation of large-scale wind power grid connections. Existing methods are typically limited to a certain fixed area when learning the information of the wind speed sequence, which cannot make full use of the spatiotemporal correlation of the wind speed sequence. To address this problem, in this paper we propose a new wind speed prediction method based on collaborative filtering against a virtual edge expansion graph structure in which virtual edges enrich the semantics that the graph can express. It is an effective extension of the dataset, connecting wind turbines of different wind farms through virtual edges to ensure that the spatial correlation of wind speed sequences can be effectively learned and utilized. The new collaborative filtering on the graph is reflected in the processing of the wind speed sequence. The wind speed is preprocessed from the perspective of pattern mining to effectively integrate various information, and the k-d tree is used to match the wind speed sequence to achieve the purpose of collaborative filtering. Finally, a model with long short-term memory (LSTM) as the main body is constructed for wind speed prediction. By taking the wind speed of the actual wind farm as the research object, we compare the new approach with four typical wind speed prediction methods. The mean square error is reduced by 16.40%, 11.78%, 9.57%, and 18.36%, respectively, which demonstrates the superiority of the proposed new method.

Taiwan, being located on a path in the west Pacific Ocean where typhoons often strike, is often affected by typhoons. The accompanying strong winds and torrential rains make typhoons particularly damaging in Taiwan. Therefore, we aimed to establish an accurate wind speed prediction model for future typhoons, allowing for better preparation to mitigate a typhoon’s toll on life and property. For more accurate wind speed predictions during a typhoon episode, we used cutting-edge machine learning techniques to construct a wind speed prediction model. To ensure model accuracy, we used, as variable input, simulated values from the Weather Research and Forecasting model of the numerical weather prediction system in addition to adopting deeper neural networks that can deepen neural network structures in the construction of estimation models. Our deeper neural networks comprise multilayer perceptron (MLP), deep recurrent neural networks (DRNNs), and stacked long short-term memory (LSTM). These three model-structure types differ by their memory capacity: MLPs are model networks with no memory capacity, whereas DRNNs and stacked LSTM are model networks with memory capacity. A model structure with memory capacity can analyze time-series data and continue memorizing and learning along the time axis. The study area is northeastern Taiwan. Results showed that MLP, DRNN, and stacked LSTM prediction error rates increased with prediction time (1–6 hours). Comparing the three models revealed that model networks with memory capacity (DRNN and stacked LSTM) were more accurate than those without memory capacity. A further comparison of model networks with memory capacity revealed that stacked LSTM yielded slightly more accurate results than did DRNN. Additionally, we determined that in the construction of the wind speed prediction model, the use of numerically simulated values reduced the error rate approximately by 30%. These results indicate that the inclusion of numerically simulated values in wind speed prediction models enhanced their prediction accuracy.

Accurate wind speed prediction is of importance for long-span cross-sea bridges. To this end, data decomposition techniques are usually employed to promote accuracy of the prediction model. Since wind speed data come sequentially, real-time decomposition should be adopted. However, real-time decomposition may degrade the accuracy due to the end effect. In this paper, a novel scheme of real-time decomposition that is a combination of truncated real-time decomposition and multi-resolution data is developed. Specifically, truncated real-time decomposition firstly denoises the data and eliminates the end effect; high-resolution data are then introduced to compensate for the information loss. Further, a novel wind speed prediction model that consists of the proposed scheme and neural networks is proposed. Specifically, two gated recurrent unit neural networks are employed to extract features from the obtained original-resolution and high-resolution data, respectively, and a multi-layer perceptron is adopted to utilize extracted features and make predictions. The proposed model is validated on the monitoring wind speed data of two long-span cross-sea bridges. Specifically, the mean absolute error and the root of mean square error of the proposed model on the two datasets are 0.334, 0.445 and 0.233 and 0.316 m/s, which are smaller than benchmark models and demonstrate superiority of the proposed model.

Time series prediction of wind speed has been widely used in wind power generation. The volatility and instability of wind speed have a large negative impact on wind turbines and power systems, which can lead to grid collapse in severe cases. Therefore, accurate wind speed prediction is crucial for wind power generation. In this paper, considering the influence of different parameters on algorithm training and prediction, an improved moth flame optimization algorithm is constructed to optimize the LSTM wind energy prediction system to obtain better performance. The system consists of three modules: data preprocessing, optimization, and prediction. The data preprocessing module uses fuzzy information granulation to blur the input data. On this basis, the combination of swarm intelligent optimization algorithm and prediction model can effectively predict wind speed time series. Taking the California wind farm as an example, the MAPE of the experiment in the short-term forecast is 3.15%, the MAPE of the medium-term forecast is 4.38%, and the MAPE of the long-term forecast is 18.28%. The experimental results show that the proposed model has obvious advantages over the previous model.

Abstract. Accurate prediction of wind speed is an important aspect of various tasks related to wind energy management such as wind turbine predictive control and wind power scheduling. The most typical characteristic of wind speed data is its persistent temporal variations. Most of the techniques reported in the literature for prediction of wind speed and power are based on statistical methods or probabilistic distribution of wind speed data. In this paper we demonstrate that deterministic forecasting methods can make accurate short-term predictions of wind speed using past data, at locations where the wind dynamics exhibit chaotic behaviour. The predictions are remarkably accurate up to 1 h with a normalised RMSE (root mean square error) of less than 0.02 and reasonably accurate up to 3 h with an error of less than 0.06. Repeated application of these methods at 234 different geographical locations for predicting wind speeds at 30-day intervals for 3 years reveals that the accuracy of prediction is more or less the same across all locations and time periods. Comparison of the results with f-ARIMA model predictions shows that the deterministic models with suitable parameters are capable of returning improved prediction accuracy and capturing the dynamical variations of the actual time series more faithfully. These methods are simple and computationally efficient and require only records of past data for making short-term wind speed forecasts within practically tolerable margin of errors.

Wind speed prediction with spatio–temporal correlation is among the most challenging tasks in wind speed prediction. In this paper, the problem of predicting wind speed for multiple sites simultaneously is investigated by using spatio–temporal correlation. This paper proposes a model for wind speed prediction with spatio–temporal correlation, i.e., the predictive deep convolutional neural network (PDCNN). The model is a unified framework, integrating convolutional neural networks (CNNs) and a multi-layer perceptron (MLP). Firstly, the spatial features are extracted by CNNs located at the bottom of the model. Then, the temporal dependencies among these extracted spatial features are captured by the MLP. In this way, the spatial and temporal correlations are captured by PDCNN intrinsically. Finally, PDCNN generates the predicted wind speed by using the learnt spatio–temporal correlations. In addition, three error indices are defined to evaluate the prediction accuracy of the model on the wind turbine array. Experiment results on real-world data show that PDCNN can capture the spatio–temporal correlation effectively, and it outperforms the conventional machine learning models, including multi-layer perceptron, support vector regressor, decision tree, etc.

Wind power is a popular renewable energy source, and the accurate prediction of wind speed plays an important role in improving the power generation efficiency of wind turbines and ensuring the normal operation of wind power equipment. Due to the instability and randomness of wind speed, it is difficult to achieve accurate prediction by traditional prediction methods. To improve the power generation efficiency of wind turbines and realize the predictability of wind speed, a hybrid wind speed prediction model based on GRUs (gated recurrent units) was constructed in this paper based on a deep neural network and feature extraction method. The hybrid model feature extraction module was implemented based on a combination of Tsfresh (a python package for time series feature extraction) and sparse PCA (sparse principal component analysis), and the network structure and other hyperparameters of the GRU module were determined through experiments. The model was validated using actual wind measurement data from a wind farm on the west coast of the United States. The results showed that the proposed model had less computational time and higher computational accuracy than the SARIMAX (seasonal auto-regressive integrated moving average with exogenous factors) and LSTM (long short-term memory) models.

Considering that more than 90% of water vapor is concentrated in the troposphere, and many weather phenomena such as cloud, fog and rain occur in the middle and lower troposphere, a short-term wind power prediction method based on meteorological data of multiple isobaric surfaces in the middle and lower troposphere is proposed. In view of the fact that the power accuracy depends greatly on the accuracy of wind speed prediction, and the current wind speed prediction in wind farms has some problems such as phase lag and system deviation, the decoupling method of wind power conversion model is considered, which firstly corrects the wind speed prediction error and then establishes the wind power conversion model. In detail, based on the multi-layer meteorological forecast data, the optimal feature combination optimization model is established with the maximum average accuracy as the objective function. On this basis, a wind speed correction model based on Conv1D was constructed. Finally, the wind speed - power conversion model is constructed by five kinds of fluctuation process classification, and the corrected wind speed is converted into power. Experimental results show that the proposed method can obtain better accuracy than using correlation coefficients to select feature combinations or only using surface data for modeling.

Combinations of physical and statistical wind speed forecasting models are frequently used in wind speed prediction problems arising in wind farms management. Artificial neural networks can be used in these models as a final step to obtain accurate wind speed predictions. The aim of this work is to determine the potential of evolutionary product unit neural networks (EPUNNs) for improving the accuracy and interpretation of these systems. Traditional neural network and EPUNN approaches have been used to develop different wind speed prediction models. The results obtained using different EPUNN models show that the functional model and the hybrid algorithms proposed provide very accurate prediction compared with standard neural networks used to solve this regression problem. One of the main advantages of the application of these EPUNNs has been the possibility of obtaining some interpretation of the non-linear relation predicted by the model, as will be shown in real data of a wind farm in Spain.

Short‐term wind speed prediction is essential for economical wind power utilization. The real‐world wind speed data are typically intermittent and fluctuating, presenting great challenges to existing shallow models. In this paper, we present a novel deep hybrid model for multistep wind speed prediction, namely, LR‐FFT‐RP‐MLP/LSTM (linear fast Fourier transform rank pooling multiple‐layer perceptron/long short‐term memory). Our hybrid model processes the local and global input features simultaneously. We leverage RP for the local feature extraction to capture the temporal structure while maintaining the temporal order. Besides, to understand the wind periodic patterns, we exploit FFT to extract global features and relevant frequency components in the wind speed data. The resulting local and global features are, respectively, integrated with the original data and are fed into an MLP/LSTM layer for the initial wind speed predictions. Finally, we leverage a linear regression layer to collaborate these initial predictions to produce the final wind speed prediction. The proposed hybrid model is evaluated using real wind speed data collected from 2010 to 2020, demonstrating superior forecasting capabilities when compared with state‐of‐the‐art single and hybrid models. Overall, this study presents a promising approach for improving the accuracy of wind speed forecasting.