Short-TermWind Power Prediction Based on Combinatorial Neural Networks

Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation.Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections.For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model,the short-term prediction of wind power based on a combined neural network is proposed.First,the Bi-directional Long Short Term Memory(BiLSTM)network prediction model is constructed,and the bi-directional nature of the BiLSTM network is used to deeply mine the wind power data information and find the correlation information within the data.Secondly,to avoid the limitation of a single prediction model when the wind power changes abruptly,the Wavelet Transform-Improved Adaptive Genetic Algorithm-Back Propagation(WT-IAGA-BP)neural network based on the combination of the WT-IAGA-BP neural network and BiLSTM network is constructed for the short-term prediction of wind power.Finally,comparing with LSTM,BiLSTM,WT-LSTM,WT-BiLSTM,WT-IAGA-BP,and WT-IAGA-BP&LSTM prediction models,it is verified that the wind power short-term prediction model based on the combination of WT-IAGA-BP neural network and BiLSTM network has higher prediction accuracy.

Rolling Generation Dispatch Based on Ultra-short-term Wind Power Forecast

The power systems economic and safety operation considering large-scale wind power penetration are now facing great challenges, which are based on reliable power supply and predictable load demands in the past. A rolling generation dispatch model based on ultra-short-term wind power forecast was proposed. In generation dispatch process, the model rolling correct not only the conventional units power output but also the power from wind farm, simultaneously. Second order Markov chain model was utilized to modify wind power prediction error state (WPPES) and update forecast results of wind power over the remaining dispatch periods. The prime-dual affine scaling interior point method was used to solve the proposed model that taken into account the constraints of multi-periods power balance, unit output adjustment, up spinning reserve and down spinning reserve.

Gaussian Kernel Based SVR Model for Short-Term Photovoltaic MPP Power Prediction

Predicting the power obtained at the output of the photovoltaic(PV)system is fundamental for the optimum use of the PV system.However,it varies at different times of the day depending on intermittent and nonlinear environmen-tal conditions including solar irradiation,temperature and the wind speed,Short-term power prediction is vital in PV systems to reconcile generation and demand in terms of the cost and capacity of the reserve.In this study,a Gaussian kernel based Support Vector Regression(SVR)prediction model using multiple input variables is proposed for estimating the maximum power obtained from using per-turb observation method in the different irradiation and the different temperatures for a short-term in the DC-DC boost converter at the PV system.The performance of the kernel-based prediction model depends on the availability of a suitable ker-nel function that matches the learning objective,since an unsuitable kernel func-tion or hyper parameter tuning results in significantly poor performance.In this study for thefirst time in the literature both maximum power is obtained at max-imum power point and short-term maximum power estimation is made.While evaluating the performance of the suggested model,the PV power data simulated at variable irradiations and variable temperatures for one day in the PV system simulated in MATLAB were used.The maximum power obtained from the simu-lated system at maximum irradiance was 852.6 W.The accuracy and the perfor-mance evaluation of suggested forecasting model were identified utilizing the computing error statistics such as root mean square error(RMSE)and mean square error(MSE)values.MSE and RMSE rates which obtained were 4.5566*10-04 and 0.0213 using ANN model.MSE and RMSE rates which obtained were 13.0000*10-04 and 0.0362 using SWD-FFNN model.Using SVR model,1.1548*10-05 MSE and 0.0034 RMSE rates were obtained.In the short-term maximum power prediction,SVR gave higher prediction performance according to ANN and SWD-FFNN.

Very Short-Term Generating Power Forecasting for Wind Power Generators Based on Time Series Analysis

In recent years, there has been introduction of alternative energy sources such as wind energy. However, wind speed is not constant and wind power output is proportional to the cube of the wind speed. In order to control the power output for wind power generators as accurately as possible, a method of wind speed estimation is required. In this paper, a technique considers that wind speed in the order of 1 - 30 seconds is investigated in confirming the validity of the Auto Regressive model (AR), Kalman Filter (KF) and Neural Network (NN) to forecast wind speed. This paper compares the simulation results of the forecast wind speed for the power output forecast of wind power generator by using AR, KF and NN.

Wind Speed Short-Term Prediction Based on Empirical Wavelet Transform, Recurrent Neural Network and Error Correction

Predicting wind speed accurately is essential to ensure the stability of the wind power system and improve the utilization rate of wind energy.However,owing to the stochastic and intermittent of wind speed,predicting wind speed accurately is difficult.A new hybrid deep learning model based on empirical wavelet transform,recurrent neural network and error correction for short-term wind speed prediction is proposed in this paper.The empirical wavelet transformation is applied to decompose the original wind speed series.The long short term memory network and the Elman neural network are adopted to predict low-frequency and high-frequency wind speed sub-layers respectively to balance the calculation efficiency and prediction accuracy.The error correction strategy based on deep long short term memory network is developed to modify the prediction errors.Four actual wind speed series are utilized to verify the effectiveness of the proposed model.The empirical results indicate that the method proposed in this paper has satisfactory performance in wind speed prediction.

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%。

基于二次分解双向门控单元新型电力系统超短期负荷预测

在新型电力系统中,电力负荷随机性和波动性较强,现有预测方法难以对其实现高精度预测。为此,提出一种基于二次分解和双向门控循环单元的超短期负荷预测模型。首先,针对电力负荷的强随机性和强波动性,利用自适应噪声完备经验模态分解对电力负荷历史序列进行初步分解,使负荷序列更加平稳。随后,对初步分解得到的强非平稳分量运用连续变分模态分解进行二次分解,降低其预测难度。最后,为充分学习电力负荷的时序特征,在预测过程构建基于双向门控循环单元的超短期电力负荷预测模型。实验结果表明,该模型相较于现有优秀预测模型有更高的预测精度。

基于CNN-BiGRU-Attention的短期电力负荷预测

针对目前电力负荷数据随机性强,影响因素复杂,传统单一预测模型精度低的问题,结合卷积神经网络(Convolutional neural network,CNN)、双向门控循环单元(Bi-directional gated recurrent unit,BiGRU)以及注意力机制(Attention)在短期电力负荷预测上的不同优点,提出一种基于CNN-BiGRU-Attention的混合预测模型。该方法首先通过CNN对历史负荷和气象数据进行初步特征提取,然后利用BiGRU进一步挖掘特征数据间时序关联,再引入注意力机制,对BiGRU输出状态给与不同权重,强化关键特征,最后完成负荷预测。试验结果表明,该模型的平均绝对百分比误差(Mean absolute percentage error,MAPE)、均方根误差(Root mean square error,RMSE)、判定系数(R-square,R~2)分别为0.167%、0.057%、0.993,三项指标明显优于其他模型,具有更高的预测精度和稳定性,验证了模型在短期负荷预测中的优势。

基于低风速功率修正和损失函数改进的超短期风电功率预测

风电功率具有较强的波动性和随机性。为进一步提升风电功率的预测精度,提出一种基于低风速功率修正和损失函数改进的超短期风电功率预测模型。该模型采用卷积神经网络、自注意力机制和双向门控循环单元捕获风电功率序列的长期时序依赖关系。为了解决低风速下待风状态神经网络难以精确拟合的问题,模型通过预测风速并结合当前时段的风电功率对低风速段的预测功率进行修正。针对参数训练的稳定性问题,模型通过改进预测策略和共享权重,引入一种多元非线性的损失函数来提取序列间的关联性。结果表明,所提模型在多项误差指标中均优于对比模型,能够有效提升超短期风电功率的预测效果。

基于BP-AHP风机状态评估的超短期风电功率动态预测研究

针对传统风电功率预测仅考虑气象因素,且无法计及风电机组真实出力状态导致预测精度较差问题,本文提出一种计及风机状态的超短期风电功率动态预测方法。首先,为能够精确评估风机状态,将BP(error back propagation, BP)算法引入层次分析法(analytic hierarchy process, AHP)的评估结构中,构建BP-AHP风机状态评估模型,实现单台风机状态评估;然后,综合考虑地形及机组排布等因素,将风电场所有风机的状态取均值作为风电场状态,利用皮尔逊相关系数衡量所评估状态与功率之间的相关性以验证评估模型合理性,并采用XGBoost构建计及风机状态的动态预测模型;最后,以陕西地区某风电场实测数据进行算例分析,验证了所提方法的可行性及有效性。

基于VMD-LILGWO-LSSVM短期风电功率预测

目的为了减小风电功率并入国家电网时产生的频率波动,提高风电功率预测精度,方法提出一种结合变分模态分解(VMD)、改进灰狼算法(LILGWO)和最小二乘支持向量机(LSSVM)的风电功率短期预测方法。首先通过VMD方法将风电功率序列分解重构成3个复杂程度性不同的模态分量,降低风电功率的波动性;其次使用LSSVM挖掘各分量的特征信息,对各分量分别进行预测,针对LSSVM模型中重要参数的选取对预测精度影响较大问题,引入LILGWO对参数进行寻优;最后将各分量预测结果叠加重构,得到最终预测风电功率。结果以宁夏回族自治区某地区风电站实际数据为例,对未来三天分别进行预测取平均值,本文方法的预测平均绝对误差(mean absolute error,MAE)为2.7068 kW,均方根误差(root mean square error,RMSE)为2.0211,拟合程度决定系数(R-Square,R^(2))为0.9769,与对比方法3~6相比,RMSE分别降低了40.93%,25.21%,14.7%,6.24%;MAE分别降低了42.34%,28.04%,16.97%,7.77%;R^(2)分别提升了4.21%,1.78%,0.82%,0.28%。预测时长方面,BP和LSSVM平均训练时间分别是10,138 s,虽然LSSVM预测时间较长但效果最好,采用PSO、GWO、LILGWO对LSSVM进行寻优后训练时间分别平均缩短了39,44,58 s。结论仿真验证了所提方法在短期风电功率预测方面的有效性。

基于多窗宽核密度估计的风电功率超短期自适应概率预测

精准的风电功率预测是保证新型电力系统安稳运行、促进风电消纳的重要手段。针对核密度估计所求分位数在不同置信度下鲁棒性差的问题,提出多窗宽核密度估计方法,根据不同置信度生成不同窗宽的核密度估计值,实现了风电功率的超短期自适应概率预测。首先,结合风电功率曲线和数据驱动模型,建立基于改进双向长短期记忆网络的风电功率超短期确定性预测模型。其次,推导了最优窗宽核密度估计方法,并基于此构建多窗宽核密度估计误差拟合模型,在不同置信度下自适应生成最优窗宽并构建预测区间。最后,基于实际运行数据验证模型的可行性与有效性。结果表明,所提模型可有效提高确定性预测的精度和概率预测的鲁棒性。

基于特征选择及ISSA-CNN-BiGRU的短期风功率预测

针对风电功率随机性大、平稳性低,以及直接输入预测模型往往难以取得较高精度等问题,提出了一种基于特征选择及改进麻雀搜索算法(ISSA)优化卷积神经网络-双向门控循环单元(CNN-BiGRU)的短期风电功率预测方法。首先,利用变分模态分解(VMD)将原始功率分解为一组包含不同信息的子分量,以降低原始功率序列的非平稳性,提升可预测性,同时通过观察中心频率方式确定模态分解数。其次,对每一分量采用随机森林(RF)特征重要度的方法进行特征选择,从风速、风向、温度、空气密度等气象特征因素中,选取对各个分量预测贡献度较高的影响因素组成输入特征向量。然后,建立各分量的CNN-BiGRU预测模型,针对神经网络算法参数难调、手动配置参数随机性大的问题,利用ISSA对模型超参数寻优,自适应搜寻最优参数组合。最后,叠加各分量的预测值,得到最终的预测结果。以中国内蒙古某风电场实际数据进行仿真实验,与多种单一及组合预测方法进行对比,结果表明,本文所提方法相比于其他方法具有更高的预测精度,其平均绝对百分比误差值达到2.644 0%;在其他4个数据集上进行的模型准确性及泛化性验证结果显示,模型平均绝对百分比误差值分别为4.385 3%、3.174 9%、1.576 1%和1.358 8%,均保持在5.000 0%以内,证明本文所提方法具有较好的预测精度及泛化能力。

CEEMDAN-WPE-CLSA超短期风电功率预测方法研究

提出了一种结合自适应噪声完全集合经验模态分解、加权排列熵、卷积神经网络、长短期记忆网络和自注意力机制的超短期风电功率预测方法。首先,利用自适应噪声完全集合经验模态分解将原始风电功率时间序列自适应分解为一系列的模态分量,降低原始序列的非线性和波动性;其次,根据加权排列熵计算各模态分量间的相似性并对相似的分量进行重组,以修正自适应噪声完全集合经验模态分解的过度分解问题,使得修正后的模态分量更具规律性;最后,将重组后的分量输入卷积长短期记忆网络进行时序建模,并利用自注意力机制对卷积长短期记忆网络的神经元权重进行重新分配,提高了卷积长短期记忆网络对输入特征不确定性的适应能力。在此基础上,明确了自注意力机制和自适应噪声完全集合经验模态分解、加权排列熵在风电功率预测中的作用机制,以及风电功率信号包含的重要物理信息,证明了自适应噪声完全集合经验模态分解、加权排列熵以及自注意力机制在风电功率信号模态分解和长短期记忆网络隐层输出权重分配中的有效性。

基于密度聚类模态分解的卷积神经网络和长短期记忆网络短期风电功率预测

近年来,随着碳达峰和碳中和“双碳”战略目标的提出,风力发电已成为可再生能源发电的关键部分。为提高风电功率短期预测的准确度,提出基于密度聚类与自适应噪声完备集成经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和卷积神经网络与长短期记忆网络结合的短期风电功率预测方法。首先,利用密度聚类将风电功率与天气特征分成不同类别的数据集,通过自适应噪声完备集成经验模态分解算法将不同类别的数据进行频域分解得到子序列分量。以此为基础,将不同的子序列分量与天气特征进行特征选择,输入到卷积神经网络与长短期记忆网络的预测模型。最后,将不同的预测结果进行叠加得到最终的预测结果。整个预测过程通过聚类、分解和特征选择,有效提高了短期风电功率预测的准确度。

基于注意力机制的IWOA-BiGRU超短期风电功率预测

超短期风电功率预测对电力系统调度及大规模风电并网具有重要作用。为得到准确可靠的风电功率预测结果,针对风电功率数据非线性和时序性的特点,提出一种基于IWOA-AT-BiGRU的超短期风电功率预测方法。首先,提出改进鲸鱼优化算法(improved whale optimization algorithm,IWOA)来优化风电功率预测模型的超参数,加速模型收敛,提高预测准确度;然后,在BiGRU中加入注意力机制(AT),AT用来加强重要信息对风功率的影响,BiGRU同时考虑数据的正反向信息,充分挖掘数据的时序特征;最后,通过某风电场实测数据进行实验,结果表明提出的方法预测准确度均高于其他对比模型,具有良好的预测性能。

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。

一种采用记忆神经网络和曲线形状修正的负荷预测方法

针对分布式电源和新型负荷容量累积造成负荷影响因素多元化和不确定性特性增强的问题,文中提出一种采用记忆神经网络和曲线形状修正的负荷预测方法。在负荷峰值预测中,采用最大信息系数计算负荷峰值与影响因素的非线性相关性,实现对输入特征的筛选;综合考虑负荷峰值序列的长短期自相关性和输入特征与负荷峰值的不同程度相关性,结合Attention机制和双向长短时记忆(bidirectional long short-term memory,BiLSTM)神经网络建立负荷峰值预测模型。在负荷标幺曲线预测中,通过误差倒数法组合相似日和相邻日,建立负荷标幺曲线预测模型;针对预测偏差的非平稳特征,利用自适应噪声的完全集成经验模态分解和BiLSTM网络建立误差预测模型,对曲线形状进行修正。应用中国北方某城市的区域电网负荷数据为算例,验证了所提模型的有效性。

基于互补集成经验模态分解和Stacking融合的短期风速组合预测模型

针对风电场短期风速预测准确度不高的问题,提出一种基于互补集成经验模态分解和Stacking融合的短期风速组合预测模型。首先,为突出短期风速的局部特征并降低建模难度,通过互补集成经验模态分解算法将短期风速分解为若干个稳定分量。然后,利用信息熵和近似熵来判定各分量的复杂度,高复杂度分量选择最小二乘支持向量机、低复杂度分量选择随机配置网络作为对应的预测模型。利用Stacking算法对每个模型的预测值进行融合,使预测精度得到提升。最后,通过一组实际的短期风速数据作为研究对象,将提出的预测模型应用于其预测。对比结果表明,所提预测模型可提高短期风速的预测精度。

基于VMD-BiLSTM-WOA的短期风电功率预测

风力发电对于解决全球能源短缺问题有重要意义,准确预测风电功率有助于风电并网的合理调度和可靠的电网运行.文章提出了一种基于变分模态分解(Variational Mode Decomposition, VMD)、双向长短期记忆网络(Bidirectional Long Short-term Memory Network, BiLSTM)以及鲸鱼优化算法(Whale Optimization Algorithm, WOA)的混合深度学习模型,以用于短期风电功率预测.首先,VMD将原始风电功率分解为多个子模态,有效减少了序列的波动性;然后对每个子模态分别建立BiLSTM模型,使用WOA对BiLSTM中的参数进行优化,以提高混合模型的效率和预测性能;最后将各个子模型的结果叠加得到最终预测结果.在实验中通过建立不同的比较模型来说明改进策略的有效性和优越性,结果表明所提的混合模型在风电功率预测中具有较高的预测精度.