Wind Speed Interval Prediction Research Articles

An improved interval prediction method for recurrence period wind speed

Based on the improved interval operation theory, an improved expression of the return period wind speed interval prediction is constructed by using an approximate first-order Taylor series expansion. According to the measured wind speed data in Beijing, Jinan, Nanjing, Wuxi, Shanghai and Shenzhen, the improved method and the traditional method are respectively used to predict the interval of the return period wind speed. Furthermore, the interval results predicted by the improved method and the traditional method are compared and analyzed under the same confidence level. Results show that the improved method has good applicability for different parameter estimation methods under the condition of certain extreme value distribution model, and the interval prediction results of the return period wind speed are basically stable. Compared with the interval results predicted by the traditional method, the interval predicted by the improved method is more likely to be close to or contain the exact solution of the return period wind speed, which has higher prediction accuracy. In addition, the calculation process of the improved method is relatively simple and can realize the simplified calculation of interval prediction.

Short-term wind speed interval prediction using improved quality-driven loss based gated multi-scale convolutional sequence model

Efficient estimation of the uncertainty associated with wind speed forecast is crucial for evaluating wind farms' power quality and operation. Typically, the performance of prediction interval (PI) generating models; is restricted in terms of computational efficiency inheritably from the sequential data processing; whereas restrictions in forecast quality are derived from the PI generation techniques. This paper presents an efficient Gated Multi-Scale Convolutional Sequence Model (GMSCSM) to forecast wind speed PIs. GMSCSM while conserving the ‘recurrence’ of LSTMs also offers ‘parallel input’ advantage of CNNs for better computational efficiency which is highly desirable for short-term forecasting models. In addition, capturing features at various scales in the sequence, GMSCSM learns both local details and global context which is vital for multi-horizon forecasts. The model generates quality PIs utilizing an improved quality-driven loss which we proposed by invoking calibration assessment in its existing definition. Forecasts generated for eight different datasets obtained from two different wind farms show an improvement of 30 % and 6 % in the average coverage width criterion index while reducing the model training time to nearly half and one third of the respective values obtained from traditional and LSTM based models which showcases the proposed model's excellent prediction capability and efficiency.

A novel dynamic spatio-temporal graph convolutional network for wind speed interval prediction

It is crucial to predict the wind speed for the utilization of renewable wind energy and the operation of transmission lines with increased capacity. The intermittency and stochastic fluctuations of wind speed pose a significant challenge for the high-quality wind speed prediction, and a novel wind speed interval prediction (WSIP) model is constructed in this study by employing the residual estimation (RE)-oriented dynamic spatio-temporal graph convolutional network (DSTGCN) approach. Firstly, a dynamic adjacency matrix is designed to obtain time-varying global spatial weight allocations among each wind speed node. Then, the spatio-temporal features are extracted by using gated recurrent units (GRUs) and GCNs to construct the wind speed graph networks. Moreover, the RE-oriented strategy incorporating the pinball loss is designed to provide a guidance the parameter training of the constructed model, thus eliminating the quantile crossings problem. As a result, the deterministic point prediction of the wind speed is expanded to the quantile-based probabilistic interval prediction. Finally, the experimental results are presented to demonstrate the validity and superiority of proposed scheme in both qualitative and quantitative performance.

A wind speed interval prediction method for reducing noise uncertainty

Due to the noise uncertainty, the conventional point prediction model is difficult to describe the actual characteristics of wind speed and lacks a description of the wind speed fluctuation range. In this paper, the kernel density estimation according to its error value is given, and then its fluctuation range is found to combine the prediction results of the test set to get its prediction range. Firstly, the singular spectrum analysis (SSA) is introduced to conduct the noise reduction, and variational modal decomposition (VMD) is performed to handle the sequences, then an improved slime mold algorithm (SMA) is proposed to optimize the VMD, and the stochastic configuration networks (SCNs) is applied to perform the prediction. Finally, the interval prediction results are calculated by fusing the point prediction error and kernel density estimation. The experimental results demonstrate that the proposed method can effectively reduce the noise interference in the wind speed prediction.

A novel spatial–temporal generative autoencoder for wind speed uncertainty forecasting

Wind speed interval prediction is one of the most long-standing challenges because of the high uncertainty and the complex spatial–temporal correlation between wind turbines. In this paper, based on variational Bayesian inference, we propose a novel spatial–temporal generative autoencoder (STGAE) model to capture the continuous probability distribution of each wind turbine on an arbitrary graph structure. In the encoding process, a parameterization strategy is used to expand the hypothesis space of the adjacency matrix rather than being limited by the distance between nodes. The multiple graph convolution layers with kernels approximated by Chebyshev polynomials adjust the collecting scope of spatial information. The bi-directional dependency is modeled by the multi-head attention to tap correlations of different time horizons effectively. The interval prediction at each node is yielded based on the probability densities of generated samples produced by the decoder. Using publicly available wind data from the Global Energy Forecasting Competition 2014, numerical experiments are conducted to compare the prediction reliability and accuracy between the proposed model and the recent state-of-the-art models. The forecasting results suggest that the proposed model significantly improves performance, especially the strong generalization ability makes it practical under different weather conditions.

A Novel Twin Support Vector Regression Model for Wind Speed Time-Series Interval Prediction

Although the machine-learning model demonstrates high accuracy in wind speed prediction, it struggles to accurately depict the fluctuation range of the predicted values due to the inherent uncertainty in wind speed sequences. To address this limitation and enhance the reliability, we propose an effective wind speed interval prediction model that combines twin support vector regression (TSVR), variational mode decomposition (VMD), and the slime mould algorithm (SMA). In our methodology, the complex wind speed series is decomposed into multiple relatively stable subsequences using the VMD method. The principal component and residual series are then subject to interval prediction using the TSVR model, while the remaining components undergo point prediction. The SMA method is employed to search for optimal parameter combinations. The prediction interval of wind speed is obtained by aggregating the forecasting results of all TSVR models for each subseries. Our proposed model has demonstrated superior performance in various applications. It ensures that the wind speed value falls within the designated interval range while achieving the narrowest prediction interval. For instance, in the spring dataset with 1-period, we obtained a predicted interval with a prediction intervals coverage probability (PICP) value of 0.9791 and prediction interval normalized range width (PINRW) value of 0.0641. This outperforms other comparative models and significantly enhances its practical application value. After adding the residual interval prediction model, the reliability of the prediction interval is significantly improved. As a result, this study presents a novel twin support vector regression model as a valuable approach for multi-step wind speed interval prediction.

Wind speed interval prediction based on multidimensional time series of Convolutional Neural Networks

Wind power, as an economical and clean energy source, has rapidly infiltrated into modern power grids. Wind speed prediction is a pivotal technology for wind power integration, which has attracted much attention from researchers and practitioners. Nevertheless, traditional methods only focus on point prediction, which is far from meeting the requirements of power system risk assessment and risk control. To fill this gap, we propose an interval prediction and analysis system based on data preprocessing, deep learning, convolutional neural networks, and interval prediction. It can effectively predict and analyze the uncertainty in short-term wind speeds. First, an original wind speed sequence is divided into high-frequency and low-frequency data using a data preprocessing strategy. After deleting the high-frequency noise data, the denoised multidimensional time series is entered as input into an optimized convolutional neural network. Subsequently, the lower and upper bounds of the interval prediction are obtained using the interval construction principle and variance of the training data set. To verify the effectiveness of the proposed hybrid system, a 10 min wind speed data set of the Shandong wind farm in China is used as an example for analysis. The experimental results show that the proposed hybrid prediction system can not only effectively predict the variation trend of wind speed series, but also accurately predict the local characteristics and volatility of wind speed with strong randomness.

Hybrid attention-based temporal convolutional bidirectional LSTM approach for wind speed interval prediction.

Precise wind speed prediction is crucial for the management of the wind power generation systems. However, the stochastic nature of the wind speed makes optimal interval prediction very complicated. In this paper, a hybrid approach consisting of improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), temporal convolutional network with attention mechanism (ATCN), and bidirectional long short-term memory network (Bi-LSTM) is proposed for wind speed interval prediction (WSIP). First, ICEEMDAN is used to pre-process the raw data by decomposing the wind signal to several intrinsic mode functions. ATCN is used to reduce the uncertainty from the denoised data and extract the important temporal and spatial characteristics. Then, Bi-LSTM is used to forecast the high-quality intervals for the wind speed. Existing approaches observe a decline in the forecasting performance when the time ahead increases. As a result, the hybrid approach is evaluated using 5-min, 10-min, and 30-min ahead WSIP. To evaluate the novelty of the proposed approach, an experiment is conducted utilising wind speed data from the Garden City, Manhattan wind farm. The experimental results demonstrate that the proposed framework outperformed the comparison models with percentage improvements of 36%, 47%, and 17% for 5-min, 10-min, and 30-min ahead WSIP.

Evolutionary quantile regression gated recurrent unit network based on variational mode decomposition, improved whale optimization algorithm for probabilistic short-term wind speed prediction

Wind energy, as clean energy, has attracted more and more attention. Wind power generation is easily threatened by the irregular fluctuation of wind speed, which interferes with the safety and stability of power system. In this study, a wind speed interval prediction method based on variational mode decomposition (VMD), phase space reconstruction (PSR), whale optimization algorithm (WOA), quantile regression (QR) and gated recurrent unit (GRU) is proposed. Firstly, the wind speed time series is decomposed into a variety of intrinsic mode functions (IMFs) through VMD to reduce the stochasticity. Secondly, all IMFs are then reconstructed using PSR to get the optimal input variables of the model. Then, the QRGRU model is optimized by the improved WOA to get the optimal QRGRU model parameters. Then, the wind speed interval prediction model of PSR-IWOA-QRGRU is established for each intrinsic mode function. Finally, the prediction results of each component are superimposed to realize the wind speed interval prediction. By checking on three different data sets, the effectiveness of the proposed method in wind speed interval prediction is proved.

A novel hybrid wind speed interval prediction model based on mode decomposition and gated recursive neural network.

Wind energy has become one of the most efficient renewable energy sources. However, the wind has the characteristics of intermittence and uncontrollability, so it is challenging to predict wind speed accurately. Considering the shortcomings of traditional wind power point predictions, a new hybrid model comprised three main modules used for data preprocessing, deterministic point prediction, and interval prediction is proposed to predict the wind speed interval. The first module, the data preprocessing module, uses variational mode decomposition (VMD), sample entropy (SE), and singular spectrum analysis (SSA) to extract the different frequency components of the initial wind speed series. The second module, the deterministic point prediction module, uses extreme learning machines (ELM), and a gated recursive unit (GRU) model to perform point prediction on the wind speed series. The third module, the interval prediction module, uses the nonparametric kernel density estimation method to construct the upper and lower bounds of the wind speed interval. In addition, the final wind speed prediction interval is obtained by integrating the prediction results of multiple interval prediction results to improve the robustness and generalization of the wind speed interval prediction. Finally, the effectiveness of the prediction performance of the proposed hybrid model is verified based on the data of two actual wind farms. The experimental results show that the proposed hybrid model can obtain the appropriate wind speed interval with high confidence and quality with different confidence levels of 95%, 90%, and 85%.

A Novel Wind Speed Interval Prediction System Based on Neural Network and Multi-objective Grasshopper Optimization

As a clean energy source, the role of wind power in the energy mix is becoming increasingly important. Reliable and high-quality wind speed prediction results are key to wind energy utilization. The traditional point prediction method cannot effectively analyze the uncertainty of wind speed, and the interval prediction model can provide the possible variation range of wind speed under a certain confidence probability and supply more uncertain information to decision makers. However, the previous interval prediction models generally ignore the random characteristics of capturing wind speed and the importance of objective selection of prediction submodels, leading to poor prediction results. To address these problems, a combined model based on data preprocessing, multi-neural network models, multi-objective optimization, and an improved interval prediction method is proposed. The model is applied to five wind speed forecasting examples in Dalian to test the prediction accuracy, multi-step prediction ability, and universality and generalization ability of the model. The experimental results show that the model proposed in this study is satisfactory for various performance evaluation indexes, has high stability and accuracy, and all the solutions obtained by the model are Pareto optimal solutions. Thus, it provides a reliable reference for the effective utilization of wind energy.

A short-term wind speed interval prediction method based on WRF simulation and multivariate line regression for deep learning algorithms

The accurate wind speed prediction is of significant importance to decrease the adverse impact of wind randomness on the power systems and help wind power grid-tied. However, the previous studies mainly focus on the point forecast, and the uncertainties of the prediction accuracy have been ignored. Moreover, utilizing WRF numerous variables to construct wind speed prediction interval (PI) based on multivariate line regression theory has rarely been studied. Therefore, a novel wind speed PI method is proposed, including three modules: data collection, decomposition, and interval prediction. Specifically, various variables, such as wind speed, wind direction, temperature, pressure, and relative humidity, are first obtained from weather research and forecasting (WRF) simulation. Secondly, variational mode decomposition (VMD) is applied to decompose these variables and actual wind speed into respective intrinsic mode functions (IMFs). Lastly, generative adversarial network (GAN) captures temporal and spatial correlation features and realizes wind speed PI based on multivariate line regression theory. Four datasets are selected to verify the performance of the proposed model. The experimental results indicate that this novel model achieves better PI accuracy compared to several baseline methods. In the case of wind speed PI of dataset1, the PI normalized average width (PINAW) values improve by 49.53%, 15.21% over VMD-back propagation (BP), VMD-long short-term memory (LSTM) in the seventh-step prediction. In summary, the proposed model outperforms other comparative models due to the lower PINAW and coverage width-based criterion (CWC) with similar PI coverage probability (PICP).

Short-Term Wind Speed Interval Prediction using LUBE based Quasi-Recurrent Neural Network

Accurate wind speed forecasting is an extremely difficult task and therefore less accurate forecasts may sometimes make the power systems vulnerable. Interval predictions on the other hand may help in maximizing the usage of integrated wind energy, as well as to reduce the adverse effects of the uncertainties introduced by the random fluctuations of wind, to the power systems. In this paper, we propose a novel wind speed interval prediction model by integrating lower upper bound estimation method (LUBE) into a quasi-recurrent neural network (QRNN). LUBE is a non-differentiable loss function requiring usage of meta-heuristic optimization techniques for training the model. By incorporating two objective functions, we proposed a new form of LUBE, using which the network can be trained with conventional stochastic gradient descent. The proposed model is evaluated for eight different datasets distributed among two wind fields. Results obtained from the computational experiments reveals that the proposed method generated narrow intervals with high coverage which helps it in achieving an improvement of 33% in coverage width criterion over the traditional models.

Wind speed interval prediction model based on variational mode decomposition and multi-objective optimization

As a potential new energy power generation technology, wind power is gradually developing into the world’s mainstream energy. In the research on wind power generation, wind speed prediction is an important part, which has been widely studied. The accurate wind speed prediction is a key part of wind power management to help wind power grid-tied. Currently, most research has focused on point prediction, which in fact does not facilitate the quantitative characterization of the endogenous uncertainty involved. However, interval prediction can avoid this deficiency and make better operation and scheduling of the wind power models. In this study, a novel interval prediction model based on wind speed distribution and multi-objective optimization is designed, which includes data noise reduction module, prediction module, and combination module based on a multi-objective salp swarm algorithm, to provide accurate forecast for power model operation and grid dispatching. The 10-minute wind speed data from three data sets in China were selected for prediction to evaluate the effectiveness of the proposed combined model. The results show that the model is not only better than the considered benchmark model, but also has good potential practical application value in wind power models.

A simple approach for short-term wind speed interval prediction based on independently recurrent neural networks and error probability distribution

Improving the quality of Wind Speed Interval prediction is important to maximize the usage of integrated wind energy as well as to reduce the adverse effects of the uncertainties, introduced by the random fluctuations of wind, to the power systems. This paper utilizes independently recurrent neural network to propose two new interval prediction frameworks. This network possesses the ability to retain memory at different lengths, which is helpful in capturing temporal features, especially for multi-horizon forecasts where the local dynamics get quite involved. In the first approach, we integrated a quantile regression loss function into this network to generate the intervals. This framework however, require to train different regressors to generate the conditional quantiles. Removing this limitation, a new simple and intuitive approach, is proposed which estimates the prediction intervals using a Gaussian function centered on the prediction and estimated error by a point prediction model and an error prediction model respectively. In our computational experiments, which involve two different wind fields contributing to eight different cases, an improvement of 43% and 12%, in average coverage width criterion index, over traditional models and LSTM based model respectively is remarkable. Thus, the proposed framework is able to produce high quality PIs while simultaneously reducing the computational cost.

A stochastic sensitivity-based multi-objective optimization method for short-term wind speed interval prediction

With the increasing penetration of wind power in renewable energy systems, it is important to improve the accuracy of wind speed prediction. However, wind power generation has great uncertainties which make high-quality interval prediction a challenge. Existing multi-objective optimization interval prediction methods do not consider the robustness of the model. Thus, trained models for wind speed interval prediction may not be optimal for future predictions. In this paper, the prediction interval coverage probability, the prediction interval average width, and the robustness of the model are used as three objective functions for determining the optimal model of short-term wind speed interval prediction using multi-objective optimization. Furthermore, a new Stochastic Sensitivity for Prediction Intervals (SS_PIs) is proposed in this work to measure the stability and robustness of the model for interval prediction. Using wind farm data from countries on two different continents as case studies, experimental results show that the proposed method yields better prediction intervals in terms of all metrics including prediction interval coverage probability (PICP), prediction interval normalized average width (PINAW) and SS_PIs. For example, at the prediction interval nominal confidence (PINC) of 85%, 90% and 95%, the proposed method has the best performance in all metrics of the USA wind farm dataset.

A novel deep interval prediction model with adaptive interval construction strategy and automatic hyperparameter tuning for wind speed forecasting

Wind energy is a renewable energy source with great development potential. However, its inherent instability and randomness have brought great challenges to the maximum utilization of wind energy. Wind speed forecasting is one of the most effective ways to mitigate these challenges, which plays an important role in the operational management and decision-making of wind power system operators. In this study, a novel wind speed interval prediction model based on gated recurrent unit, Variational Mode Decomposition, and Particle Swarm Optimization was proposed. The original wind speed sequence was decomposed into several smoother sub-sequences through the Variational Mode Decomposition algorithm, and corresponding sub-models were established based on the gated recurrent unit. To better supervise the training process, artificial prediction intervals with adaptive adjustment strategies were devised. Moreover, the Particle Swarm Optimization algorithm was adopted to search for the optimal superposition weights of PIs to achieve the integral optimization of the model. The qualitative and quantitative performance of the proposed method has been fully tested and verified in a series of real cases.

A Novel Wind Speed Interval Prediction Based on Error Prediction Method

Wind speed interval prediction plays an important role in wind power generation. In this article, a new interval construction model based on error prediction is proposed. The variational mode decomposition is used to decompose the complex wind speed time series into simplified modes. Two types of GRU models are built for wind speed prediction and error prediction. Prediction error for each mode is given a weight and accumulated to obtain the width of the prediction interval. The particle swarm optimization algorithm is applied to search for the optimal weights of the prediction errors. Experiments considering eight cases from two wind fields are conducted by using methods of interval construction in the literature for comparison with the proposed model. The result shows that the proposed model can obtain prediction intervals with higher quality.

A new lower and upper bound estimation model using gradient descend training method for wind speed interval prediction

AbstractAs a clean and renewable energy source, wind energy has achieved remarkable growth around the world. Wind power/speed interval prediction has become an indispensable area of focus regarding the efficient dispatch of wind energy. As an important interval prediction method, the traditional lower and upper bound estimation (LUBE) has been a prevalent approach and a fundamental branch of energy prediction. However, the traditional LUBE model suffers from a low training efficiency owing to a lack of the gradient descent (GD) training mechanism. In this study, an improved LUBE model was designed using a novel training scheme based on the GD method for better efficiency and greater prediction performance. Initially, the new objective functions, which are continuous and differential, meeting the requirements of the GD method, were designed to obtain the best prediction interval (PI) quality with a narrower PI width and greater coverage probability. Then, different loss function forms have been proposed and compared, with the new Huber loss function having been confirmed to be more effective than other traditional loss functions. Finally, the new LUBE model with an objective part and adapting to the GD training method was constructed. Both traditional and improved LUBE models with different loss functions were compared experimentally, and the results indicate that the improved LUBE model with a Huber loss function significantly reduces the training time and improves the quality of the PI.

Temporal convolutional networks interval prediction model for wind speed forecasting

Wind speed interval prediction is one of the most elusive and long-standing challenges in wind power production. As a data source with intermittent and fluctuant characteristics, wind speed time series require highly nonlinear temporal features for the prediction tasks. In this paper, a novel interval prediction model is proposed based on temporal convolutional networks to forecast wind speed. A temporal convolutional networks architecture layer, multiple fully connected layers using tanh activation function and an end-to-end sorting layer are respectively served as input, hidden and output layers of the temporal convolutional networks interval prediction model which can generate prediction intervals directly. Additionally, an adaptive interval construction optimization strategy is put forward to devise training labels for learning of model. Eight cases from two wind fields are implemented to test and verify the proposed method. Specially, experiments have been designed to compare the prediction accuracy and reliability between the proposed model and the most recent state-of-the-art models. The forecasting results suggest that the proposed model has a significant performance improvement on both prediction interval coverage probability and prediction interval width criteria and thus can be a practical tool for wind speed forecasting.