Wind Speed Forecasting Model Research Articles

Weather forecasting based on data-driven and physics-informed reservoir computing models.

In response to the growing demand for the global energy supply chain, wind power has become an important research subject among studies in the advancement of renewable energy sources. The major concern is the stochastic volatility of weather conditions that hinder the development of wind power forecasting approaches. To address this issue, the current study proposes a weather prediction method divided into two models for wind speed and atmospheric system forecasting. First, the data-based model incorporated with wavelet transform and recurrent neural networks is employed to predict the wind speed. Second, the physics-informed echo state network was used to learn the chaotic behavior of the atmospheric system. The findings were validated with a case study conducted on wind speed data from Turkmenistan. The results suggest the outperformance of physics-informed model for accurate and reliable forecasting analysis, which indicates the potential for implementation in wind energy analysis.

Ultra-short-term wind speed forecasting based on EMD-VAR model and spatial correlation

Accurate wind speed forecasting is conducive to reduce the risk of power system from wind power uncertainty, which is of great significance to power system operation. However, it’s very difficult to achieve satisfactory results in wind speed forecasting due to the complex fluctuation characteristics of wind speed series. This study proposes a novel EMD-VAR wind speed forecasting model based on the wind speed data of multiple adjacent measuring points with high correlation. To achieve better experimental results with high accuracy and strong stability, multiple adjacent spatial sites are used to balance the information and variance of the forecasting model. Empirical mode decomposition (EMD) methods are utilized to remove the noise in the original data and multiple intrinsic mode function (IMF) components are obtained. For each IMF component, the corresponding vector autoregressive (VAR) model is established for the spatial groups. The final forecasting result is obtained by summarizing the forecasting results of all the IMF components. To validate the accuracy and stability of the proposed model, wind speed data sets in four seasons are used for experimental prediction. Experiments show that this method can effectively improve the accuracy and guarantee the reliability of wind speed forecasting in each season.

Comparative models for multi-step ahead wind speed forecasting applied for expected wind turbine power output prediction

This paper investigates six of the most widely used wind speed forecasting models for a combination of statistical and physical methods for the purpose of Wind Turbine Power Generation (WTPG) prediction in Cameroon. Statistical method based on both single static and dynamic neural networks architectures and two hybrid neural networks architectures in comparison to ARIMA model are employed for multi-step ahead wind speed forecasting in two Datasets in Bapouh, Cameroon. The physical method is used to estimate 1 day ahead expected WTPG for each Dataset using the previous predicted wind speed from better forecasting models. The obtained results of multi-step ahead forecasting showed that the ARIMA and nonlinear autoregression with exogenous input neural network (NARXNN) models perform well the wind speed forecasting than other forecasting models in both Datasets. The better performances of ARIMA are achieved with one-step ahead and two-step ahead forecasting, while NARXNN is better with one-step ahead forecasting. But NARXNN models have more computational time than other models such as ARIMA models. Furthermore, the effectiveness of employed hybrid method for WTPG prediction is proven.

A combined model for short-term wind speed forecasting based on empirical mode decomposition, feature selection, support vector regression and cross-validated lasso.

BackgroundThe planning and control of wind power production rely heavily on short-term wind speed forecasting. Due to the non-linearity and non-stationarity of wind, it is difficult to carry out accurate modeling and prediction through traditional wind speed forecasting models.MethodsIn the paper, we combine empirical mode decomposition (EMD), feature selection (FS), support vector regression (SVR) and cross-validated lasso (LassoCV) to develop a new wind speed forecasting model, aiming to improve the prediction performance of wind speed. EMD is used to extract the intrinsic mode functions (IMFs) from the original wind speed time series to eliminate the non-stationarity in the time series. FS and SVR are combined to predict the high-frequency IMF obtained by EMD. LassoCV is used to complete the prediction of low-frequency IMF and trend.ResultsData collected from two wind stations in Michigan, USA are adopted to test the proposed combined model. Experimental results show that in multi-step wind speed forecasting, compared with the classic individual and traditional EMD-based combined models, the proposed model has better prediction performance.ConclusionsThrough the proposed combined model, the wind speed forecast can be effectively improved.

An ultra-short-term wind speed forecasting model based on time scale recognition and dynamic adaptive modeling

The forecast of wind speed is prerequisite for wind power prediction, which is one of the most effective means of promoting wind power absorption. However, when modeling for wind speed sequences with different fluctuations, most existing researches ignore the influence of time scale of wind speed fluctuation period, let alone the low compatibility between training and testing samples that severely limit the training performance of forecasting model. To improve the accuracy of wind speed and wind power forecasting, an ultra-short-term wind speed forecasting model based on time scale recognition and dynamic adaptive modeling is proposed in this paper. First, a series of wind processes are divided from the historical wind speed sequence according to the natural variation characteristics of wind speed. Second, we divide all the wind processes into two patterns based on their time scale, and an SVC model with input features extracted from meteorological data is built to identify the time scale of the current wind process. Third, for a specifically identified wind process, the complex network algorithm is applied in data screening to select high compatible training samples to train the forecast model dynamically for current input. Simulation indicates that the proposed approach presents higher accuracy than benchmark models using the same forecasting algorithms but without considering the time scale and data screening.

WSFNet: An efficient wind speed forecasting model using channel attention-based densely connected convolutional neural network

This paper introduces a novel deep neural network (WSFNet) to efficiently forecast multi-step ahead wind speed. WSFNet forms the basis of the stacked convolutional neural network (CNN) with dense connections of different blocks equipped with the channel attention (CA) module. Dense connections create direct transition paths between the input and all subsequent convolutional blocks. This encourages the reuse of all activations at the network input without loss of gradients in subsequent layers. The CA modules contribute significantly to the performance of the network by suppressing non-useful features extracted by each convolution block. In the proposed method, variational mode decomposition (VMD) was utilized to provide an effective preprocessing and improve the forecasting ability. The case study was conducted on publicly available data from Sotavento Galicia (SG) wind farm. In the evaluations, three variants of the proposed network were analyzed and compared with state-of-the-art deep learning methods. When the results were analyzed, the overall correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), and symmetric mean absolute percentage error (SMAPE) were obtained as 0.9705, 0.7383, 0.5826, and 0.0466, respectively. The obtained results indicate that the proposed method achieved a competitive performance and can be effectively used for smart-grid operations.

Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism

Accurate and reliable wind speed forecasting is important for the dispatch and management of wind power generation systems. However, existing forecasting models based on the data decomposition approach only perform time–frequency analysis of wind speed series, while ignoring the coupling relationship between other meteorological variables and wind speed in the time and frequency domains. Therefore, a novel hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with an attention mechanism is proposed in this study. Specifically, singular spectrum analysis is used to decrease the noise of the original multivariate series. Multivariate empirical mode decomposition is used to decompose the denoised series into their respective intrinsic mode functions and residuals. Further, a new hybrid deep learning algorithm, which combines a convolutional neural network optimized via an attention mechanism and a bidirectional long short-term memory network, is proposed to extract spatiotemporal correlation features between all intrinsic mode functions and residuals and to perform final wind speed forecasting. Finally, three experiments were performed to evaluate the performance of the proposed model comprehensively. The experimental results indicate that the proposed model is superior to other baseline models in terms of both accuracy and effectiveness.

A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm

Due to expanding global environmental issues and growing energy demand, wind power technologies have been studied extensively. Accurate and robust short-term wind speed forecasting is crucial for large-scale integration of wind power generation into the power grid. However, the seasonal and stochastic characteristics of wind speed make forecasting a challenging task. This study adopts a novel hybrid deep learning-based evolutionary approach in an attempt to improve the accuracy of wind speed prediction. This hybrid model consists of a bidirectional long short-term memory neural network, an effective hierarchical evolutionary decomposition technique and an improved generalised normal distribution optimisation algorithm for hyper-parameter tuning. The proposed hybrid approach was trained and tested on data gathered from an offshore wind turbine installed in a Swedish wind farm located in the Baltic Sea with two forecasting horizons: ten-minutes ahead and one-hour ahead. The experimental results indicated that the new approach is superior to six other applied machine learning models and a further seven hybrid models, as measured by seven performance criteria.

Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks

The goal of sustainable development can be attained by the efficient management of renewable energy resources. Wind energy is attracting attention worldwide due to its renewable and sustainable nature. Accurate wind speed prediction is essential for the stable functioning of wind turbines to generate wind power. However, the flexible and intermittent nature of wind speed makes accurate wind speed forecasting a challenging task. The proposed wind speed forecasting framework combines the features of various data decomposition techniques and Bidirectional Long Short Term Memory (BiDLSTM) networks. Presently, Data Decomposition models such as the Wavelet Transform are extensively employed for wind speed forecasting to improve the accuracy of the forecasting models. Hence, in this paper, various data decomposition techniques that can denoise the signal are investigated and applied to partition the input time series into several high and low-frequency signals. The data decomposition methods, namely, Wavelet Transform, Empirical Model Decomposition, Ensemble Empirical Mode Decomposition, and Empirical Wavelet Transform, have been applied to denoise the dataset. The low and high-frequency sub-series are forecasted separately using Bidirectional LSTM networks, and the forecasting outcomes of low and high-frequency signals are aggregated to get the final forecasting results. The empirical results establish that the proposed EWT- based hybrid model outperforms other decomposition-based models in accuracy and stability. The performance of the EWT-BiDLSTM model is further compared with Bidirectional LSTM networks with skip connections. The experimental results substantiate that the proposed decomposition-based hybrid deep BiDLSTM models with skip connections exhibit better prediction accuracy than other models.

A Novel Hybrid Model Based on an Improved Seagull Optimization Algorithm for Short-Term Wind Speed Forecasting

Wind energy is a clean energy source and is receiving widespread attention. Improving the operating efficiency and economic benefits of wind power generation systems depends on more accurate short-term wind speed predictions. In this study, a new hybrid model for short-term wind speed forecasting is proposed. The model combines variational modal decomposition (VMD), the proposed improved seagull optimization algorithm (ISOA) and the kernel extreme learning machine (KELM) network. The model adopts a hybrid modeling strategy: firstly, VMD decomposition is used to decompose the wind speed time series into several wind speed subseries. Secondly, KELM optimized by ISOA is used to predict each decomposed subseries. The ISOA technique is employed to accurately find the best parameters in each KELM network such that the predictability of a single KELM model can be enhanced. Finally, the prediction results of the wind speed sublayer are summarized to obtain the original wind speed. This hybrid model effectively characterizes the nonlinear and nonstationary characteristics of wind speed and greatly improves the forecasting performance. The experiment results demonstrate that: (1) the proposed VMD-ISOA-KELM model obtains the best performance for the application of three different prediction horizons compared with the other classic individual models, and (2) the proposed hybrid model combining the VMD technique and ISOA optimization algorithm performs better than models using other data preprocessing techniques.

Wind Speed Forecasting Using the Stationary Wavelet Transform and Quaternion Adaptive-Gradient Methods

Accurate wind speed forecasting is a fundamental requirement for advanced and economically viable large-scale wind power integration. The hybridization of the quaternion-valued neural networks and stationary wavelet transform has not been proposed before. In this paper, we propose a novel wind-speed forecasting model that combines the stationary wavelet transform with quaternion-valued neural networks. The proposed model represents wavelet subbands in quaternion vectors, which avoid separating the naturally correlated subbands. The model consists of three main steps. First, the wind speed signal is decomposed using the stationary wavelet transform into sublevels. Second, a quaternion-valued neural network is used to forecast wind speed components in the stationary wavelet domain. Finally, the inverse stationary wavelet transform is applied to estimate the predicted wind speed. In addition, a softplus quaternion variant of the RMSProp learning algorithm is developed and used to improve the performance and convergence speed of the proposed model. The proposed model is tested on wind speed data collected from different sites in China and the United States, and the results demonstrate that it consistently outperforms similar models. In the meteorological terminal aviation routine (METAR) dataset experiment, the proposed wind speed forecasting model reduces the mean absolute error, and root mean squared error of predicted wind speed values by 26.5% and 33%, respectively, in comparison to several existing approaches.

Long-term wind speed prediction using artificial neural network-based approaches

<abstract> <p>In the current scenario, worldwide renewable energy systems receive renewed interest because of the global reduction of greenhouse gas emissions. This paper proposes a long-term wind speed prediction model based on various artificial neural network approaches such as Improved Back-Propagation Network (IBPN), Multilayer Perceptron Network (MLPN), Recursive Radial Basis Function Network (RRBFN), and Elman Network with five inputs such as wind direction, temperature, relative humidity, precipitation of water content and wind speed. The proposed ANN-based wind speed forecasting models help plan, integrate, and control power systems and wind farms. The simulation result confirms that the proposed Recursive Radial Basis Function Network (RRBFN) model improves the wind speed prediction accuracy and minimizes the error to a minimum compared to other proposed IBPN, MLPN, and Elman Network-based wind speed prediction models.</p> </abstract>

A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer

Reliable and accurate wind speed forecasting (WSF) is fundamental for efficient exploitation of wind power. In particular, high accuracy short-term WSF (ST-WSF) has a significant impact on the efficiency of wind power generation systems. Due to the non-stationarity and stochasticity of the wind speed (WS), a single model is often not sufficient in practice for the accurate estimation of the WS. Hybrid models are being proposed to overcome the limitations of single models and increase the WS forecasting performance. In this paper, a new hybrid WSF model is developed based on long short-term memory (LSTM) network and decomposition methods with grey wolf optimizer (GWO). In the pre-processing stage, the missing data is filled by the weighted moving average (WMA) method, the WS time series (WSTS) data are smoothed by WMA filtering and the smoothed data are used as model input after Z-score normalization. The forecasting model is formed by the combination of a single model, a decomposition method and an advanced optimization algorithm. Successively, the hybrid WSF model is developed by combining the LSTM and decomposition methods, and optimizing the intrinsic mode function (IMF) estimated outputs with a grey wolf optimizer (GWO). The developed non-linear hybrid model is utilized on the data collected from five wind farms in the Marmara region, Turkey. The obtained experimental results indicate that the proposed combined model can capture non-linear characteristics of WSTS, achieving better forecasting performance than single forecasting models, in terms of accuracy.

A hesitant fuzzy wind speed forecasting system with novel defuzzification method and multi-objective optimization algorithm

Owing to the nondeterministic nature of wind speed, the conventional fuzzy time series forecasting model has difficulty in establishing a common membership level. Therefore, in this study, the fuzzy series forecasting model was improved based on hesitant fuzzy sets. A hesitant fuzzy wind speed forecasting system with a novel defuzzification method and multiobjective optimization algorithm was developed. First, an advanced decomposition model is employed to extract the effective feature and remove the noise component from the raw wind speed series. Then, the universe of discourse is partitioned into equal and unequal intervals by multifuzzification methods and merged by aggregating hesitant information. A multiobjective intelligent optimization algorithm is applied to determine the optimal weights of different intervals accurately and stably. Furthermore, a novel defuzzification model based on an ordered weighted averaging operator and a regular increasing monotone quantifier is proposed to calculate the final forecasting results. The crucial strengths of the developed system are verifying the possibility of enhancing the performance of wind speed forecasting models by improving conventional fuzzy time series forecasting models and integrating them with decomposition models and artificial-intelligence models. Typical wind speed series datasets with different resolutions were selected to evaluate the performance of the proposed system, and experimental results prove that the proposed system outperforms other comparison models with high forecasting accuracy and computing efficiency.

A hybrid neural network model for short-term wind speed forecasting based on decomposition, multi-learner ensemble, and adaptive multiple error corrections

Under the dual stimulus of the new energy demand and the increasing competitiveness of wind energy, the construction of wind speed prediction models began to be placed in a position that cannot be ignored. To overcome the challenges brought by wind speed fluctuations to wind speed forecasting, this paper proposes a novel hybrid wind speed forecasting deep model. The model has three modules, including data preprocessing, multi-learner ensemble, and adaptive multiple error correction. We used four real wind series in Xinjiang, China to verify the performance of the model. The results of the case study show that: (a) The proposed hybrid deep model for wind speed forecasting is superior to several state-of-the-art models in terms of both forecasting stability and forecasting accuracy; (b) The proposed hybrid deep model is excellent in multi-step forecasting, taking the site #1 as an example, the MAEs of the proposed model are 0.0250 m/s, 0.0417 m/s, and 0.0570 m/s, respectively.

Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting

Although the random forest (RF) model is a powerful machine learning tool that has been utilized in many wind speed/power forecasting studies, there has been no consensus on optimal RF modeling strategies. This study investigates three basic questions which aim to assist in the discernment and quantification of the effects of individual model properties, namely: (1) using a standalone RF model versus using RF as a correction mechanism for the persistence approach, (2) utilizing a recursive versus direct multi-step forecasting strategy, and (3) training data availability on model forecasting accuracy from one to six hours ahead. These questions are investigated utilizing data from the FINO1 offshore platform and Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) C1 site, and testing results are compared to the persistence method. At FINO1, due to the presence of multiple wind farms and high inter-annual variability, RF is more effective as an error-correction mechanism for the persistence approach. The direct forecasting strategy is seen to slightly outperform the recursive strategy, specifically for forecasts three or more steps ahead. Finally, increased data availability (up to ∼8 equivalent years of hourly training data) appears to continually improve forecasting accuracy, although changing environmental flow patterns have the potential to negate such improvement. We hope that the findings of this study will assist future researchers and industry professionals to construct accurate, reliable RF models for wind speed forecasting.

A novel deep learning intelligent clustered hybrid models for wind speed and power forecasting

Wind energy is playing a compromising role in the new generation of sustainable energy and promising to increase more. Forecasting of the fluctuated wind speed and its output power is playing an essential role in the smart power system grid. The wind power integration is based on the accuracy of the wind speed and power forecasting models. This paper is proposing highly accurate hybrid deep learning clustered models for wind speed and power forecasting using different artificial intelligent systems for optimal performance. Various combinations of Recurrent Kalman Filter (RKF), Fourier Series (FS), Wavelet (WNN) and Artificial Neural Network (ANN) are used in this work. Twelve different hybrid models are proposed and tested. The novelty of this work is the applied clustered segments with the deep learning hybrid models to improve the aggregated system performance. This work is validated by using different unseen data set with the proposed models as well as using K-fold cross validation method. All the proposed models are performing well with high accurate results, but the hybrid clustered model of WNN and RKF outperforms all other models.

An efficient online sequential extreme learning machine model based on feature selection and parameter optimization using cuckoo search algorithm for multi-step wind speed forecasting

Accurate wind speed forecasting (WSF) has become increasingly important to overcome the adverse effects of stochastic nature of the wind on wind power generation. This paper proposes a multi-step hybrid online WSF model by combining online sequential extreme learning machine (OSELM), optimized variational mode decomposition (OVMD) and cuckoo search optimization algorithm (CSO). OVMD decomposes the wind speed series into subseries, and CSO selects the input features for each subseries. Multi-step forecasting for each subseries is performed using OSELM model optimized by CSO. Finally, the forecasting results are obtained by the aggregate calculations. The proposed model has been examined by using 10-min average wind speed data collected in monsoon and winter seasons from a supervisory control and data acquisition system of a 1.5 MW wind turbine situated in central dry zone of Karnataka, India. The results reveal that the model proposed captures the nonlinear characteristics of the wind speed in a better manner in comparison with the batch learning approach, giving accurate wind speed forecasts. This can help wind farms to estimate the wind power in a location efficiently.

Daily Wind Speed Forecast Model at an Altitude of 120 Meters, Pak Phanang District, Nakhon Si Thammarat Province

Daily Wind Speed Forecast Model at an Altitude of 120 Meters, Pak Phanang District, Nakhon Si Thammarat Province

Multi-step wind speed forecasting model based on wavelet matching analysis and hybrid optimization framework

Accurate wind speed forecasting is beneficial to the management of the wind power system. A hybrid WPD-DA-NAR wind speed forecasting model under moving window framework is proposed in this study. The WPD (Wavelet Packet Decomposition) is utilized to process the original wind speed time series. Since the mother wavelet function is the core component of the WPD, the matching relationship between the predictor and 17 different mother wavelet functions is discussed. The optimal mother wavelet for the application of the proposed hybrid model is determined. The NAR (Nonlinear Autoregressive) network is employed to build the forecasting models for decomposed sub-layers. A hybrid optimization framework base on the DA (Dragonfly algorithm) is adopted to optimize the NAR network. In order to capture the characters of wind speed time series and update model accordingly, the optimized NAR is applied under a moving window framework. The proposed hybrid model is compared with 8 existing models. The experimental results indicated that: (a) the dmey wavelet provides the best results among all the included 17 mother wavelet functions; (b) the proposed hybrid WPD-DA-NAR model under moving window framework has the best performance in all steps and metrics, compared with 8 existing models.