Climate and Forecast Mode Simulations for Antarctica:Implications for Temperature and Wind

Climate and forecast mode simulations with the regional climate model HIRlam-ECHAM（HIRHAM） are evaluated over a pan-Antarctic domain. The ability of the model to simulate temperature and wind profiles in the troposphere is quantified by comparing its results with radiosonde data acquired from the Davis station for January and July 2007. Compared to the climate mode, the forecast mode was found to deliver improved results for temperature and wind simulations at the near surface and in the lower troposphere. The main remaining model bias found was the under-representation of low-level wind jets. Based on ensemble simulations, it is shown that a distinct internal variability is inherent in the climate mode simulations, and associated areas of reduced predictability over Antarctica are identified.

基于主客观环流分型的强降水数值预报MODE检验方法及其在2019年暖季东北地区的应用

本文构建了基于主客观环流分型的强降水数值预报空间检验(MODE)方法框架,并利用该框架对欧洲中期天气预报中心全球模式(ECMWF)和中国气象局区域中尺度数值天气预报模式(CMA_MESO)的2019年暖季东北地区强降水预报进行检验。结果表明,2019年暖季东北地区54个强降水日的环流型可分为:西风槽型(15个)、副热带高压影响型(13个)、急流型(5个)、西部(12个)和东部冷涡型(9个)。其中,西风槽型和急流型以区域性强降水为主,模式对其强降水发生与否的预报能力强,TS评分较高;西部、东部冷涡型强降水的局地性强,模式对其强降水发生与否的预报能力差,TS评分低;副热带高压影响型也以区域性强降水为主,模式对其强降水发生与否的预报能力也比较强,但是对其强降水质心位置、强度、面积等属性预报偏差较大,TS评分也相对较低。另外,从两种模式预报性能对比看,CMA_MESO强降水强度和面积预报较实况普遍偏强,虽然其预报的TS评分一般高于ECMWF,但其对强降水预报的空报率也都比ECMWF大,对强降水的属性预报偏差一致性一般也低于ECMWF,其预报的可订正性整体上不及ECMWF。

Wind Speed Prediction Based on Improved VMD-BP-CNN-LSTM Model

Amid the randomness and volatility of wind speed, an improved VMD-BP-CNN-LSTM model for short-term wind speed prediction was proposed to assist in power system planning and operation in this paper. Firstly, the wind speed time series data was processed using Variational Mode Decomposition (VMD) to obtain multiple frequency components. Then, each individual frequency component was channeled into a combined prediction framework consisting of BP neural network (BPNN), Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) after the execution of differential and normalization operations. Thereafter, the predictive outputs for each component underwent integration through a fully-connected neural architecture for data fusion processing, resulting in the final prediction. The VMD decomposition technique was introduced in a generalized CNN-LSTM prediction model;a BPNN model was utilized to predict high-frequency components obtained from VMD, and incorporated a fully connected neural network for data fusion of individual component predictions. Experimental results demonstrated that the proposed improved VMD-BP-CNN-LSTM model outperformed other combined prediction models in terms of prediction accuracy, providing a solid foundation for optimizing the safe operation of wind farms.

Empirical Mode Decomposition-k Nearest Neighbor Models for Wind Speed Forecasting

Hybrid model is a popular forecasting model in renewable energy related forecasting applications. Wind speed forecasting, as a common application, requires fast and accurate forecasting models. This paper introduces an Empirical Mode Decomposition (EMD) followed by a k Nearest Neighbor (kNN) hybrid model for wind speed forecasting. Two configurations of EMD-kNN are discussed in details: an EMD-kNN-P that applies kNN on each decomposed intrinsic mode function (IMF) and residue for separate modelling and forecasting followed by summation and an EMD-kNN-M that forms a feature vector set from all IMFs and residue followed by a single kNN modelling and forecasting. These two configurations are compared with the persistent model and the conventional kNN model on a wind speed time series dataset from Singapore. The results show that the two EMD-kNN hybrid models have good performance for longer term forecasting and EMD-kNN-M has better performance than EMD-kNN-P for shorter term forecasting.

Precipitation Forecast Test Based on MODE

Abstract By testing and analyzing BJ-RUC forecast of one precipitation process, MODE was introduced. MODE could give objective comparison from position of precipitation falling zone, shape and direction, and reflect intensity difference between forecast and actual situation, which comprehensively reflected precipitation forecast performance of the model, and was close to subjective judgment thinking of forecaster.

Storm-scale ensemble forecast based on breeding of growth modes

How to obtain fast-growth errors, which is comparable to the actual forecast growth error, is a crucial problem in ensemble forecast （EF）. The method, Breeding of Growth Modes （BGM）, which has been used to generate perturbations for medium-range EF at NCEP, simulates the development of fast-growth errors in the analysis cycle, and is a reasonable choice in capturing growing errors modes, especially for extreme weather by BGM. An ideal supercell storm, simulated by Weather Research Forecast model （WRF）, occurred in central Oklahoma on 20 May 1977. This simulation was used to study the application of BGM methods in the meso-scale strong convective Ensemble Prediction System （EPS）. We compared the forecasting skills of EPS by different pertubation methods, like Monte-Carlo and BGM. The results show that the ensemble average forecast based on Monte-Carlo with statistics meaning is superior to the single-deterministic prediction, but a less dynamic process of the method leads to a smaller spread than expected. The fast-growth errors of BGM are comparable to the actual short-range forecast error and a more appropriate ensemble spread. Considering evaluation indexes and scores, the forecast skills of EPS by BGM is higher than Monte-Carlo＇s. Furthermore, various breeding cycles have different effects on precipitation and non-precipitation fields, confirmation of reasonable cycles need consider balance between variables.

Identification of expected seismic activity areas by forecasting complex seismic-mode parameters in Uzbekistan

In this paper, the author proposed a methodology to reveal expected seismic activation places for coming years by a complex of forecasting parameters of a seismic mode. Areas in Uzbekistan where currently observed anomalies in various parameters of a seismic mode has been revealed. By number of displayed abnormal signs the areas has been ranked based on probability of occurrence of strong earthquakes there. It has prepared schemes of the synoptic forecast of expected seismic activation places in case of occurrence of strong earthquakes in the Central-Asian region.

白条猪价格预测模型构建

【目的】增强农产品价格预测准确度,为农产品价格的有效预测提供参考。【方法】以河南省白条猪每周平均批发价格为研究对象,提出一种基于序列分解、主成分分析和神经网络(CEEMDAN-PCA-CNN-LSTM)的白条猪价格预测方法。首先,使用自适应白噪声完全集合模态分解方法(CEEMDAN)对白条猪价格序列进行分解;其次,选用皮尔逊相关系数筛选影响价格波动的相关因素;再次,利用主成分分析(PCA)对影响因素及分解得到的子序列降维处理并作为原始价格序列的特征值,并行输入到作为编码器的卷积神经网络(CNN)中进行特征提取;最后,引入长短期记忆网络(LSTM)作为解码器输出得到预测结果。将该方法应用于河南省白条猪每周平均价格数据,与LSTM、门控循环单元(GRU)、CNN、基于卷积的长短期记忆网络(ConvLSTM)模型进行比较。【结果】CEEMDAN-PCA-CNN-LSTM组合模型预测方法得到的平均绝对误差分别降低了44.95%、27.30%、28.13%、43.17%。【结论】CEEMDAN-PCA-CNN-LSTM模型对于河南省白条猪市场价格的预测性能更优,有助于相关部门针对河南省白条猪价格波动做出科学决策。

基于CEEMD-SE的CNN&LSTM-GRU短期风电功率预测

为进一步提升短期风电功率的预测精度,提出了一种基于互补集合经验模态分解-样本熵(complementary ensemble empirical mode decomposition-sample entropy,CEEMD-SE)的卷积神经网络(convolutional neural network,CNN)和长短期记忆-门控循环单元(longshorttermmemory-gatedrecurrentunit,LSTM-GRU)的短期风电功率预测模型。首先,利用互补集合经验模态分解将原始风电功率序列分解为若干本征模态函数(intrinsic mode function,IMF)分量和一个残差(residual,RES)分量,利用样本熵算法将相近的分量进行重构;其次,搭建卷积神经网络和长短期记忆网络的并行网络结构,提取数据的局部特征和时序特征,并将特征融合后输入门控循环单元网络中进行学习预测;最后,通过算例进行验证,结果表明采用该模型后预测精度得到了有效提升,其均方根误差降低了15.06%、平均绝对误差降低了15.22%、决定系数提高了1.91%。

基于双层优化VMD-LSTM的农村超短期电力负荷预测

稳定的供电是农村发展建设的有力保障,而电力负荷水平是建设效果的重要衡量标准,因此建立精确的负荷预测模型可以更准确直观显现电力负荷情况,为供电公司制定决策提供有力支撑。由于LSTM负荷预测模型在数据预测方面存在收敛性差、预测精度不高等问题,为提高模型的预测精度,提出一种基于双层优化VMD-LSTM的超短期电力负荷预测方法。首先提出麻雀算法优化变分模态分解(sparrow variational mode decomposition,SVMD),通过SVMD将原始数据转化为模态分量(intrinsic mode functions,IMF);其次采用改进樽海鞘群算法(association salp swarm algorithm,ASSSA)优化LSTM模型。通过引入4种策略增强标准樽海鞘算法优化能力;最后将各模态分量分别代入到新模型并进行叠加预测。选取辽宁省某市某乡村10kV变压器真实历史负荷数据,以均方根误差(RMSE)、平均绝对误差(MAE)、平均绝对百分比误差(MAPE)、拟合度(R^(2))作为评价指标,并与其他基础预测模型进行对比,结果表明,改进后的算法在计算精度、稳定性方面均优于其他基础预测模型。

宽度-深度融合时频分析的径流智能预测方法

为解决现有基于LSTM的径流预测模型易陷入局部最优的问题,提出了基于VMD-LSTMBLS(variational mode decomposition-LSTM-broad learning system)的径流预测模型。将宽度学习系统与LSTM结合,针对径流序列多噪音特点,采用时频分析方法中的变分模态分解,将径流时间序列的一维时域信号变换到二维时频平面,减少噪声对预测结果的影响。仿真结果表明:与基线模型及现有基于LSTM的径流预测模型相比,该模型的预测精度有较为明显的提高。

基于双模态分解的发电站母线短期负荷预测

母线负荷预测是电力系统运营和规划中至关重要的一项任务,针对电力负荷数据的非线性强以及影响因素多等问题,提出了一种基于双模态分解、深度学习和注意力机制的负荷预测模型。首先,对输入数据进行经验模态分解(Empirical mode decomposition,EMD),通过K-means聚类分析对复杂度相似的分量进行集合得到三个组合分量。其次,使用变分模态分解(Variational mode decomposition, VMD)对组合分量再次进行分解得到不同分量,使用麻雀搜索算法(Sparrow search algorithm,SSA)对变分模态分解的参数进行优化。再次,将变分模态分解得到的分量与影响因素连接并输入长短期记忆网络(Long short-term memory network, LSTM),通过注意力机制挖掘数据内部的相关性,并使用SSA对LSTM网络的参数进行优化。最后,采用宁夏某电站一年的负荷数据进行验证,经过与不同模型的对比分析,所提模型有更高的预测精度。

基于改进Croston方法的多需求模式零备件预测

维修备件管理是提高产线可靠性、实现降本增效的关键。针对具备间歇性与随机性特征的维修备件需求预测问题,提出了基于改进Croston方法的备件需求预测模型。依据Syntetos准则基于间断性与波动性特征将备件需求划分为4类。针对含有波动性特征的需求,基于Croston方法主要思想将备件需求预测分解为需求发生状态预测和需求量预测两类问题,设计了集合经验模态分解(ensemble empirical mode decomposition,EEMD)-长短期记忆网络集成(long short-term memory,LSTM)预测模型。EEMD方法将剧烈波动序列分解为若干相对平稳的分量,进而采用LSTM方法对各分量进行预测。针对含有间断性特征的需求,引入信号处理技术中的信号调制技术,将需求发生状态0-1二值序列进行连续化处理。所提方法解决了备件需求波动性强、间断性大的难题,已应用于湖北中烟武汉卷烟厂,证明了方法的优越性与可行性。

基于经验模态分解和深度学习的短期风电功率预测

精准的风电功率预测有利于全网电力平衡、系统安全稳定运行和节能减耗。提出一种基于经验模态分解(empirical mode decomposition, EMD)、核主成分分析(kernel principal component analysis, KPCA)和长短期记忆(long short-term memory, LSTM)神经网络的短期风功率预测模型。采用EMD技术将多维气象序列分解为多个固有模态分量,以挖掘原始数据的主要特征并消除噪声;引入KPCA进行降维处理,提取数据的非线性特征;使用LSTM神经网络对特征提取的序列进行学习并完成预测,获得风电功率预测的最终结果。使用所提出的模型对新疆某一风电场风电功率进行预测,将预测结果与其他模型对比。结果表明,该预测模型能改善预测性能,降低风电功率预测误差。

基于新闻情感分析和区间分解的汇率预测研究

汇率序列具有非线性和连续变化等特点,其细节波动是一系列事件和新闻综合影响的结果.然而,现有区间预测模型难以量化重大事件和公众情绪的影响,导致其缺乏广泛的适用性,且传统区间分解方法存在上下界混叠的缺陷.因此,论文从新冠疫情冲击出发,提出一种基于新闻情感分析和区间分解的汇率波动实时预测模型.首先,基于Snownlp情感词典对外汇新闻文本进行情感分析,获得相应的情感分数.另外,构建全球恐惧指数(the global fear index,简称GFI)以量化新冠疫情的影响,并将其与芝加哥期权交易所波动率(the Chicago board options exchange volatility index,简称VIX指数)相结合作为汇率的影响因素.然后,提出一种新的区间经验模态分解(interval empirical mode decomposition,简称IEMD)方法对区间汇率序列进行多尺度分解,并根据样本熵重构得到高、中、低频区间序列和残差项.其次,利用极限学习机(extreme learning machine,简称ELM)、多层感知机(multi-layer perceptron,简称MLP)、随机森林(random forest,简称RF)和二次曲面支持向量回归(quadric surface support vector regression,简称QSSVR)分别对不同特征的子序列进行组合预测,以提高预测结果的准确性和稳定性.最后,利用论文方法对美元兑人民币、澳元兑人民币和瑞士法郎兑人民币3种汇率进行实证预测分析,结果表明,论文模型适用于重大事件影响下的汇率区间波动预测,与现有方法相比具有较高的预测精度.

基于经验模态分解和优化BiLSTM的短期负荷预测

针对电力负荷数据的非线性和不稳定性问题,提出了一种基于经验模态分解改进麻雀搜索算法双向长短期记忆神经网络相结合的EMD ISSA BiLSTM预测模型。首先采用EMD处理非线性负荷数据,将原始负荷数据分解为多个不同尺度的本征模态函数(IMF),引入反向学习策略和Levy飞行策略分别改进麻雀搜索算法(SSA)的收敛速度慢和容易陷入局部最优问题,利用改进麻雀搜索算法(ISSA)对BiLSTM神经网络进行参数寻优。然后再利用优化后的BiLSTM模型对每个分量进行预测,并将各预测结果叠加组合,得到整个负荷序列的预测结果。最后通过实际算例分析,证明该方法相对于传统的预测方法具有更好的预测精度和稳定性,可作为一种有效的短期负荷预测方法。

基于极点对称模态分解的中长期径流预报组合模型

为提高径流预报精度,解决径流序列分解后高频分量波动范围大、预报精度差的问题,基于极点对称模态分解法(ESMD)平稳化处理技术将径流序列分解,通过分析不同频率分量特征,择优选取预报方法,结合粒子群优化最小二乘支持向量机(PSO-LSSVM)全局优化和非线性建模能力及适应性强的特点,对高频分量进行预测,利用BP神经网络非线性映射能力和逼近任意非线性函数的优势对中低频分量和趋势分量进行预报,构建了ESMD-PSO-LSSVM-BP组合预报模型,对西江干流上中下游三座水文站的年、月尺度径流开展中长期径流预报。结果表明,对不同频率分量采用不同预报方法的组合模型可以有效提高径流预报精度。

基于二次分解和乌鸦搜索算法优化组合模型的超短期风速预测

针对风速的波动性和随机性等特点,提出一种基于二次分解和乌鸦搜索算法优化组合模型的超短期风速预测方法。该方法的基本思路是构造基于变分模态分解、样本熵和奇异谱分析的二次分解的方法,将原始风速序列分解为不同的子序列,并对这些子序列分别建立预测模型,最后重构。对变分模态分解的子序列建立基于长短时记忆网络的深度学习模型预测,而残差序列进行二次分解后的子序列建立乌鸦搜索算法优化的组合预测模型预测。最后,对子序列进行重构并得到最终的预测结果。使用实际的风速观测资料开展模拟实验,结果表明:在3个风电场中,所提模型与其他模型相比平均相对误差分别提升了30.07%、37.56%和37.40%,验证了混合模型在超短期风速预测中的有效性和稳定性,以及在不同数据集上的泛化性能。

基于EMD-PCA-LSTM的短期风电功率预测研究

精准的风电功率预测有利于全网电力平衡、系统安全稳定运行和节能减耗,提出一种基于EMD-PCA-LSTM的短期风电功率预测模型。先采用经验模态分解技术将多维气象序列分解为多个固有模态分量,以挖掘原始数据的主要特征并消除噪声。再引入主成分分析进行降维处理,提取数据的非线性特征,最后使用长短期记忆神经网络进行预测。通过与多种预测模型进行比较,证明了该模型在预测精度方面的卓越表现。

计及误差信息的自适应超短期风速预测模型

为提升超短期风速预测精度,提出一种计及误差信息的自适应混合预测模型。应用自适应噪声的完备集合经验模态分解模型与鲸鱼优化的变分模态分解模型分别对风速样本数据与预测误差进行分解,同时计算各子序列的模糊熵以判断序列复杂程度。在此基础上,应用鲸鱼优化的长短期网络预测复杂程度较高的序列,差分自回归移动平均模型预测复杂程度较低的序列。最后,将初始风速预测结果和风速误差预测值相加得到基于误差修正的超短期风速预测值。结果表明,修正预测误差与考虑分解策略可有效提升点预测的性能,与基准模型相比,所提模型在多场景下均具备优良的预测精度。