Short-Term Wind Speed Prediction Using Signal Preprocessing Technique and Evolutionary Support Vector Regression

Short-term wind speed prediction is beneficial to guarantee the safety of wind power utilization and reduce the cost of wind power generation. As a kind of the powerful artificial intelligent algorithms, support vector regression (SVR) has been successfully employed in solving forecasting problems. However, due to the intrinsic complexity and multi-patterns of wind speed fluctuations, it is regarded as one of the most challenging applications for wind speed prediction. To alleviate the influence of complexity and capture these different patterns, this study proposes a novel approach named SIE–WDA–GA–SVR for short-term wind speed prediction, which applies the seasonal information extraction (SIE) and wavelet decomposition algorithm (WDA) into hybrid model that integrates the genetic algorithm (GA) into SVR. First, the proposed approach uses SIE to decompose the original wind speed into seasonal and trend components, and the seasonal indices are calculated by SIE. Second, the proposed approach uses WDA to decompose the trend component into both the approximate and the detailed scales. Third, the proposed approach uses GA–SVR to forecast the approximated and detailed scales, respectively. Then, the prediction values of the trend component can be obtained by integrating the prediction values of the approximated scale into the prediction values of the detailed scale. By integrating the seasonal indices into the prediction values of trend component, we can obtain the final forecasting results of the original wind speed. Moreover, the partial autocorrelation function is used to determine the number of input dimension for the SVR, and the GA is used to select the parameters of the SVR. Four real wind speed datasets are used as test samples to verify the proposed approach. Experimental results indicate that the proposed approach outperforms other benchmark models in four statistical error measures, and can improve the forecasting accuracy of wind speed.     

基于经验模式分解和极限学习机的铀资源价格预测方法

针对国际铀资源价格预测问题, 提出一种基于经验模式分解(EMD)、相空间重构(PSR) 和极限学习机(ELM) 的非线性组合预测方法. 首先通过EMD分解, 将原始价格序列分解为若干固有模态分量(IMF), 按频率高低将各IMF 分组叠加成3 个新序列; 然后在重构相空间的基础上构建不同的ELM模型, 分别对各IMF 序列进行预测; 最后对预测结果进行合成. 将该方法应用于实际铀资源价格预测, 并与径向基神经网络(RBF) 方法及单独ELM方法进行比较, 仿真结果表明该方法预测精度有明显的提高.

     

EMD与NARX神经网络的风电场总功率组合预测

探索构建对风电场总功率进行直接预测的高精度组合预测算法。考虑到风速的非平稳性导致风电总功率表现为非平稳时间序列,采用NARX神经网络作为初步预测模型,提出了经验模态分解与NARX神经网络相结合的混合预测模型。对风电场总功率非平稳时间序列进行经验模态分解,得到不同频带本征模式分量的平稳序列。对不同频带的平稳分量建立相应的NARX神经网络预测模型,并将各分量模型的预测值进行等权求和得到最终预测值。此外,为研究不同时间间隔对预测结果的影响,采用某大型风电场时间间隔为5 min与15 min的数据进行实验。预测结果表明,提出的组合预测模型适合于总功率预测,其预测效果比传统模型的效果更佳,且时间间隔为5 min的数据比时间间隔为15 min的数据预测精度更高。     

基于EMD和RBFNN的冷负荷组合预测模型

针对冷负荷预测问题,提出了一种基于相空间重构（PSR）、经验模态分解(EMD) 和径向基神经网络(RBFNN) 的 冷负荷组合预测模型。该模型首先利用经验模态分解方法,把冷负荷序列分解为少数模态分量,然后利用分组分量法将其分 为多个高频子分量、总低频分量和残余量,最后以PSR为基础对各分量利用RBFNN方法建模并将预测结果重构。该方法应 用于实际冷站负荷预测后,与单一RBFNN、SVM、LSSVM及基于EMD的SVM、基于EMD的RBFNN5类方法进行比较,结果 表明该方法对冷负荷预测精度有明显提高。     

A variational mode decompoisition approach for analysis and forecasting of economic and financial time series

The empirical mode decomposition (EMD) has been successfully applied to adaptively decompose economic and financial time series for forecasting purpose. Recently, the variational mode decomposition (VMD) has been proposed as an alternative to EMD to easily separate tones of similar frequencies in data where the EMD fails. The purpose of this study is to present a new time series forecasting model which integrates VMD and general regression neural network (GRNN). The performance of the proposed model is evaluated by comparing the forecasting results of VMD-GRNN with three competing prediction models; namely the EMD-GRNN model, feedforward neural networks (FFNN), and autoregressive moving average (ARMA) process on West Texas Intermediate (WTI), Canadian/US exchange rate (CANUS), US industrial production (IP) and the Chicago Board Options Exchange NASDAQ 100 Volatility Index (VIX) time series are used for experimentations. Based on mean absolute error (MAE), mean absolute percentage error (MAPE), and the root mean of squared errors (RMSE), the analysis results from forecasting demonstrate the superiority of the VMD-based method over the three competing prediction approaches. The practical analysis results suggest that VMD is an effective and promising technique for analysis and prediction of economic and financial time series.     

Design of Neural Network Based Wind Speed Prediction Model Using GWO

The prediction of wind speed is imperative nowadays due to the increased and effective generation of wind power. Wind power is the clean, free and conservative renewable energy. It is necessary to predict the wind speed, to implement wind power generation. This paper proposes a new model, named WT-GWO-BPNN, by integrating Wavelet Transform (WT), Back Propagation Neural Network (BPNN) and Grey Wolf Optimization (GWO). The wavelet transform is adopted to decompose the original time series data (wind speed) into approximation and detailed band. GWO – BPNN is applied to predict the wind speed. GWO is used to optimize the parameters of back propagation neural network and to improve the convergence state. This work uses wind power data of six months with 25, 086 data points to test and verify the performance of the proposed model. The proposed work, WT-GWO-BPNN, predicts the wind speed using a three-step procedure and provides better results. Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean absolute percentage error (MAPE) and Root mean squared error (RMSE) are calculated to validate the performance of the proposed model. Experimental results demonstrate that the proposed model has better performance when compared to other methods in the literature.     

基于关联规则的PSO-Elman短期风速预测

传统神经网络在短期风速预测中,存在易陷入局部极值和动态性能不足等问题,从而导致风速预测精度较低。为了提高风速预测精度,提出一种基于关联规则的粒子群优化Elman神经网络风速预测模型。利用粒子群算法优化Elman神经网络模型参数,以提高算法的收敛速度,避免陷入局部极值,以得到最优的预测值。同时结合关联规则分析考虑气象因素,采用Apriori算法对风速与其他气象因素进行关联规则挖掘,并利用得到的关联规则对风速预测值进行修正与补偿。实验结果表明,所提出的预测模型的预测效果比传统模型的效果更佳,同时验证了结合关联规则考虑气象因素能够降低风速预测误差。     

Adaptive transfer learning in deep neural networks: Wind power prediction using knowledge transfer from region to region and between different task domains

Transfer learning (TL) in deep neural networks is gaining importance because, in most of the applications, the labeling of data is costly and time consuming. Additionally, TL also provides an effective weight initialization strategy for deep neural networks. This paper introduces the idea of adaptive TL in deep neural networks (ATL‐DNN) for wind power prediction. Specifically, we show in case of wind power prediction that adaptive TL of the deep neural networks system can be adaptively modified as regards training on a different wind farm is concerned. The proposed ATL‐DNN technique is tested for short‐term wind power prediction, where continuously arriving information has to be exploited. Adaptive TL not only helps in providing good weight initialization, but also in utilizing the incoming data for effective learning. Additionally, the proposed ATL‐DNN technique is shown to transfer knowledge between different task domains (wind power to wind speed prediction) and from one region to another region. The simulation results show that the proposed ATL‐DNN technique achieves average values of 0.0637, 0.0986, and 0.0984 for the mean absolute error, root mean squared error, and standard deviation error, respectively.     

基于混合卷积神经网络算法的风场预测研究

在农业生产中,准确的风速预报对农作物安全防范有着至关重要的作用。针对云南地区的高海拔和多山,基于卷积神经网络框架,提出了卷积长短时序分析神经网络-卷积门控循环单元神经网络(ConvLSTM-ConvGRU)混合风速预测模型。通过神经网络框架的改进,有效的提高了模型对风场空间特征的提取。利用美国国家环境预报中心(NCEP)提供的再分析风速数据集,使用ConvLSTM、ConvGRU、ConvLSTM-ConvGRU混合模型分别对云南地区的风速进行。实验结果表明：ConvLSTM-ConvGRU混合风速预测模型能够有效对云南地区风场进行预测,相较于另外两个模型提高了预测准确度。     

Locally recurrent neural networks for wind speed prediction using spatial correlation

This paper deals with the wind speed prediction in wind farms, using spatial information from remote measurement stations. Owing to the temporal complexity of the problem, we employ local recurrent neural networks with internal dynamics, as advanced forecast models. To improve the prediction performance, the training task is accomplished using on-line learning algorithms based on the recursive prediction error (RPE) approach. A global RPE (GRPE) learning scheme is first developed where all adjustable weights are simultaneously updated. In the following, through weight grouping we devise a simplified method, the decoupled RPE (DRPE), with reduced computational demands. The partial derivatives required by the learning algorithms are derived using the adjoint model approach, adapted to the architecture of the networks being used. The efficiency of the proposed approach is tested on a real-world wind farm problem, where multi-step ahead wind speed estimates from 15 min to 3 h are sought. Extensive simulation results demonstrate that our models exhibit superior performance compared to other network types suggested in the literature. Furthermore, it is shown that the suggested learning algorithms outperform three gradient descent algorithms, in training of the recurrent forecast models.     

Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model

High precision and reliable wind speed forecasting have become a challenge for meteorologists. Convective events, namely, strong winds, thunderstorms, and tornadoes, along with large hail, are natural calamities that disturb daily life. For accurate prediction of wind speed and overcoming its uncertainty of change, several prediction approaches have been presented over the last few decades. As wind speed series have higher volatility and nonlinearity, it is urgent to present cutting-edge artificial intelligence (AI) technology. In this aspect, this paper presents an intelligent wind speed prediction using chicken swarm optimization with the hybrid deep learning (IWSP-CSODL) method. The presented IWSP-CSODL model estimates the wind speed using a hybrid deep learning and hyperparameter optimizer. In the presented IWSP-CSODL model, the prediction process is performed via a convolutional neural network (CNN) based long short-term memory with autoencoder (CBLSTMAE) model. To optimally modify the hyperparameters related to the CBLSTMAE model, the chicken swarm optimization (CSO) algorithm is utilized and thereby reduces the mean square error (MSE). The experimental validation of the IWSP-CSODL model is tested using wind series data under three distinct scenarios. The comparative study pointed out the better outcomes of the IWSP-CSODL model over other recent wind speed prediction models.     

A case study on a hybrid wind speed forecasting method using BP neural network

Wind energy, which is intermittent by nature, can have a significant impact on power grid security, power system operation, and market economics, especially in areas with a high level of wind power penetration. Wind speed forecasting has been a vital part of wind farm planning and the operational planning of power grids with the aim of reducing greenhouse gas emissions. Improving the accuracy of wind speed forecasting algorithms has significant technological and economic impacts on these activities, and significant research efforts have addressed this aim recently. However, there is no single best forecasting algorithm that can be applied to any wind farm due to the fact that wind speed patterns can be very different between wind farms and are usually influenced by many factors that are location-specific and difficult to control. In this paper, we propose a new hybrid wind speed forecasting method based on a back-propagation (BP) neural network and the idea of eliminating seasonal effects from actual wind speed datasets using seasonal exponential adjustment. This method can forecast the daily average wind speed one year ahead with lower mean absolute errors compared to figures obtained without adjustment, as demonstrated by a case study conducted using a wind speed dataset collected from the Minqin area in China from 2001 to 2006.     

Short-term wind speed forecasting based on a hybrid model

Wind power is currently one of the types of renewable energy with a large generation capacity. However, operation of wind power generation is very challenging because of the intermittent and stochastic nature of the wind speed. Wind speed forecasting is a very important part of wind parks management and the integration of wind power into electricity grids. As an artificial intelligence algorithm, radial basis function neural network (RBFNN) has been successfully applied into solving forecasting problems. In this paper, a novel approach named WTT–SAM–RBFNN for short-term wind speed forecasting is proposed by applying wavelet transform technique (WTT) into hybrid model which hybrids the seasonal adjustment method (SAM) and the RBFNN. Real data sets of wind speed in Northwest China are used to evaluate the forecasting accuracy of the proposed approach. To avoid the randomness caused by the RBFNN model or the RBFNN part of the hybrid model, all simulations in this study are repeated 30 times to get the average. Numerical results show that the WTT–SAM–RBFNN outperforms the persistence method (PM), multilayer perceptron neural network (MLP), RBFNN, hybrid SAM and RBFNN (SAM–RBFNN), and hybrid WTT and RBFNN (WTT–RBFNN). It is concluded that the proposed approach is an effective way to improve the prediction accuracy.     

结合注意力机制与LSTM的短期风电功率预测模型

风力发电预测在电力系统的运行中发挥着重要作用。现有风电功率的短期预测模型因风速的复杂性和随机性,难以确定风速与风电功率的非线性映射关系,导致预测精度降低。提出一种结合变分模态分解、双阶段注意力机制、误差修正模块与深度学习算法的短期风电功率预测模型。通过对原始数据进行互信息特征选择,获得与风电功率相关性较强的特征,并对其进行信号预处理,利用变分模态分解对多维特征序列进行分解,得到具有一定中心频率的模态分量,以降低各个特征序列的复杂性和非平稳性。采用基于双阶段注意力机制与编解码架构的长短时记忆（LSTM）神经网络对模态分量进行训练与预测,得到初始预测误差。在此基础上,利用误差修正模块对初始预测误差进行变分模态分解和修正,从而提高模型的预测精度。实验结果表明,与自回归移动平均模型、标准编解码结构的LSTM模型相比,该预测模型的平均绝对误差最高可降低约87%,具有较优的预测性能。     

Multiscale prediction of wind speed and output power for the wind farm

This paper focuses on establishing the multiscale prediction models for wind speed and power in wind farm by the average wind speed collected from the history records. Each type of the models is built with different time scales and by different approaches. There are three types of them that a short-term model for a day ahead is based on the least squares support vector machine (LSSVM), a medium-term model for a month ahead is on the combination of LSSVM and wavelet transform (WT), and a long-term model for a year ahead is on the empirical mode decomposition (EMD) and recursive least square (RLS) approaches. The simulation studies show that the average value of the mean absolute percentage error (MAPE) is 4.91%, 6.57% and 16.25% for the short-term, the medium-term and the long-term prediction, respectively. The predicted data also can be used to calculate the predictive values of output power for the wind farm in different time scales, combined with the generator’s power characteristic, meteorologic factors and unit efficiency under various operating conditions.     

基于K-means和改进KNN算法的风电功率短期预测系统

为提高风电功率短期预测的准确性,针对KNN(K-Nearest neighbor algorithm)算法在风电功率预测中的不足,提出了基于K-means和改进KNN算法的风电功率短期预测方法;利用K-means聚类方法确定风电历史样本的类别,对KNN算法中搜索相似历史样本集的方式进行了改进和优化,构建了预测模型,并采用C/S架构实现了预测系统的设计;该系统具有自修正功能,能够随着预测次数的增加,不断修正预测模型,逐渐降低预测的误差率;以吉林省某风电场历史数据为样本进行了仿真分析,结果显示该算法与其它算法相比平均绝对误差和均方根误差最大下降1.08%和0.48%,运算时间提升了5.45%,在风电功率超短期多步预测中具有推广应用价值。     

基于EMD-PSO-LSSVM的碳排分解集成预测方法

二氧化碳排放量的发展趋势作为能够反映各国减排措施的指标之一,近些年来受到广泛关注.为了缓解碳排放数据的非线性和波动性对预测精度造成的影响,提出一种高效的分解集成预测方法用于预测二氧化碳的年排放量.碳排的原始序列数据被经验模态分解(empirical mode decomposition, EMD)方法分解为不同频率的振动模块和残差项,粒子群优化算法(particle swarm optimization, PSO)优化后的最小二乘支持向量机(least squares support vector machine, LSSVM)用于预测每个分解模块.选取世界上12个国家的真实碳排数据进行实例验证,预测结果表明:EMD方法能够有效提高碳排预测的精准度;与其他预测模型相比,分解集成预测方法能够将平均绝对误差(mean absolute error, MAE)的均值最少提高46.46%,最多提高90.09%,将平均Pearson相关系数(Pearson correlation coefficient, PCC)值最少提高10.45%,最多提高45.10%.     

Applying the Hybrid Model of EMD,PSR, and ELM to Exchange Rates Forecasting

Financial time series forecasting has been a challenge for time series analysts and researchers because it is noisy, nonstationary and chaotic. To overcome this limitation, this study uses empirical mode decomposition (EMD) and phase space reconstruction (PSR) to assist in the task of financial time series forecasting. In addition, we propose an approach that combines these two data preprocessing methods with extreme learning machine (ELM). The approach contains four steps as follows. (1) EMD is used to decompose the dynamics of the exchange rate time series into several components of intrinsic mode function (IMF) and one residual component. (2) The IMF and residual time series phase space is reconstructed to reveal its unseen dynamics according to the optimum time delay \(\tau \) and embedding dimension m. (3) The reconstructed time series datasets are divided into two datasets: training and testing, in which the training datasets are used to build ELM models. (4) A regression forecast model is set up for each IMF as well as the residual component by using ELM. The final prediction results are obtained by compositing the prediction values. To verify the effectiveness of the proposed approach, four exchange rates are chosen as the forecasting targets. Compared with some existing state-of-the-art models, the proposed approach yields superior results. Academically, we demonstrated the validity and superiority of the proposed approach that integrates EMD, PSR, and ELM. Corporations or individuals can apply the results of this study to acquire accurate exchange rate information and reduce exchange rate expenses.     

基于经验模态分解与多分支神经网络的超短期风功率预测

风功率预测是实现风电场监控及信息化管理的重要基础,风功率超短期预测常用于平衡负荷、优化调度,对预测精度有较高的要求。由于风电场环境复杂、风速不确定性因素较多,风功率时序信号往往具有非平稳性和随机性。循环神经网络（RNN）适用于时间序列任务,但无周期、非平稳的时序信号会增加网络学习的难度。为了克服非平稳信号在预测任务中的干扰,提高风功率预测精度,提出了一种结合经验模态分解与多分支神经网络的超短期风功率预测方法。首先将原始风功率时序信号通过经验模态分解（EMD）以重构数据张量,然后用卷积层和门控循环单元（GRU）层分别提取局部特征和趋势特征,最后通过特征融合与全连接层得到预测结果。在内蒙古某风场实测数据集上的实验结果表明,与差分整合移动平均自回归（ARIMA）模型相比,所提方法在预测精度方面有将近30%的提升,验证了所提方法的有效性。     

基于多尺度时空图神经网络的污染物浓度预测

基于深度学习的细粒度污染物浓度预测是一种新兴且具有前景的方法,如何充分利用气象、空间和时间等3大信息是其关键.为了协同融合3大信息,提出一种基于多尺度时空图神经网络的污染物浓度预测模型.该模型利用空气质量模型动态构建多尺度的时空图神经网络,学习污染物之间的动态时空关系.具体为:利用图神经网络学习污染物之间的多尺度空间关系,采用空气质量模型HYSPLIT构建图的结点和边属性,通过基于注意力机制的GRU (gate recurrent unit)学习污染物浓度之间的时序关系.该模型不仅充分考虑了气象、空间和时间3大影响因素,还将3个因素联动起来统一到一个框架内协同学习.该方法与传统的机理模型方法相比具有灵活部署、易于实施的特点.实际项目数据集和公开数据集上的实验表明:与现有先进的基于图神经网络的方法相比,该方法预测的污染物浓度平均绝对误差降低了0.6左右,对称平均绝对百分比误差降低0.005左右.