小波及混沌学习神经网络在短期电力负荷预测中的应用

该文提出了采用小波和神经网络混合模型进行电力系统短期负荷预测方法。首先基于小波多分辨率分析方法将负荷序列分解成具有不同频率特征的序列。然后,根据分解后的各个分量的特点构造不同的神经网络模型对各分量分别进行预测。神经网络算法采用混沌学习算法,与传统BP算法相比,该算法利用混沌轨道的游动性使系统能够跳出局域极值的束缚而寻求全局最优点,这样克服了BP学习算法所存在的本质问题,可以加快网络学习速度和提高学习精度。最后对各分量预测信号进行重构得到最终预测结果。在构建网络模型时,该文考虑了气候因素的影响,并把它作为网络的一组输入点。实验结果表明基于这一方法的负荷预测系统具有较好的精度及稳定性。     

LSTM模型在电力系统负荷预测中的应用

为解决数据随时间变化的电力系统短期负荷预测问题,本文阐述、采用和总结线性回归,普通BP神经网络,GRU神经网络,LSTM神经网络在电力系统短期负荷预测中应用的理论基础和方法,分别针对神经网络的多种预测方法进行归纳总结。LSTM即长短期记忆网络,拥有门机制,可以选择性的遗忘和记忆过去的信息,特别能记忆一个序列时间段的信息。本文以某城市2019年全年的电力负荷数据为基础,该数据集包含城市350天的每天48个时间点的16800条数据,通过python绘图,解决对比不同算法之间的性能差异问题,最终得到LSTM在电力系统短期负荷预测上性能最优的结果。通过结果分析,得出利用电力负荷时间序列具有历史依赖性的特点,使用LSTM深度神经网络与传统神经网络进行了对比预测,证明了深度学习在电力负荷预测领域的适用性和精确性。     

蚁群神经网络在短期负荷预测的应用

为了进一步提高BP神经网络的性能,实现准确、快速预测电力系统负荷的目的,将蚁群算法(ACA)作为BP神经网络的学习算法,建立了一种新的蚁群神经网络(AcAN)预测模型.对某电力系统短期负荷预测的计算实例表明,基于蚁群神经网络的负荷预测方法与传统的BP神经网络预测方法相比,具有较强的自适应能力和较好的效果.     

自适应变系数粒子群—径向基神经网络模型在负荷预测中的应用

为了提高短期电力负荷预测精度,提出了一种自适应变系数粒子群-径向基函数神经网络混合优化算法(AVCPSO-RBF).实现了径向基神经网络参数优化.建立了基于该优化算法的短期负荷预测模型,利用贵州电网历史数据进行短期负荷预测.仿真表明,该方法的收敛速度和预测精度优于传统径向基神经网络方法和粒子群-RBF神经网络方法及基于混沌理论的神经网络模型,该优化算法克服了径向基神经网络和传统的粒子群优化方法的缺点,改善了径向基神经网络的泛化能力,提高了贵州电网短期负荷预测的精度,各日预测负荷的平均百分比误差可控制在1.7%以内.该算法可有效用于电力系统的短期负荷预测.     

基于支持向量机的网格负载信息预测模型

提出了采用小波分析和最小二乘支持向量机(LS-SVM)混合模型对网格负载信息进行预测。该模型首先基于小波多分辨率分析对非平稳的网格负载样本做序列分解,得到不同尺度下的负载分量,然后利用LS-SVM对不同尺度的分量进行预测,最后通过对各分量预测信息进行重构得到相应的预测值。实验结果表明,使用本模型进行短期负荷预测比传统小波神经网络方法可以获得更好的预测精度。     

基于支持向量机的网格负载信息预测模型

提出了采用小波分析和最小二乘支持向量机（LS-SVM）混合模型对网格负载信息进行预测。该模型首先基于小波多分辨率分析对非平稳的网格负载样本做序列分解,得到不同尺度下的负载分量,然后利用LS-SVM对不同尺度的分量进行预测,最后通过对各分量预测信息进行重构得到相应的预测值。实验结果表明,使用本模型进行短期负荷预测比传统小波神经网络方法可以获得更好的预测精度。     

基于Elman网络的电力负荷预测研究

电力系统负荷与诸多影响因素之间是一种强耦合、多变量、严重非线性的关系,且这种关系具有动态性。传统预测方法精度不高,而采用动态回归神经网络(Elman)能更直接、更有效地反映系统的动态特性。该文建立了基于Elman神经网络的电力负荷预测模型,通过MATLAB仿真预测,对比Elman神经网络和BP神经网络的预测效果。仿真实验证明了Elman神经网络具有良好的动态特性、较快的训练速度、高精度等特点,表明Elman预测模型是一种新颖、可靠的负荷预测方法。     

改进粒子群—BP神经网络模型的短期电力负荷预测

为了准确、快速、高效地预测电网短期负荷,提出了改进的粒子群算法(MPSO),并与BP算法相结合,形成改进的粒子群—BP(MPSO-BP)神经网络算法,用此算法训练神经网络,实现了神经网络参数优化,得到了基于MPSO-BP算法的神经网络模型。综合考虑气象、天气、日期类型等影响负荷的因素,进行电网短期负荷预测。算例分析表明,与传统BP神经网络法和PSO-BP神经网络方法相比,该方法改善了BP神经网络的泛化能力,预测精度高,收敛速度快,对电力系统短期负荷具有良好的预测能力。     

基于EMD和PSO-SVM的电力系统中期负荷预测

预测精度是衡量电力系统负荷预测模型性能的一个重要指标.针对电力系统负荷数据序列是一种典型的具有用期性和随机性的非线性非平稳时间序列的特点,为了提高负荷预测的精度,提出了运用EMD解析出其中代表负荷不同特性分量的预测模型.根据各分量的变化规律,分别选择合适的核函数,并利用PSO优化SVM参数,将各分量的预测结果重构后得到最终预测值.通过EUNITE国际电力负荷预测竞赛数据的仿真实例,验证了该预测模型能够有效提高电力负荷的预测精度,且速度较快,稳定性较强.     

基于级联网络的电力系统短期负荷预测

电力系统短期负荷预测对电力系统运行设计具有十分重要的意义。因此，在分析了电力负荷运行曲线的基础上，提出了一种基于级联模糊神经网络的预测模型。该模型采用基于神经网路理论的模糊模型参数辨识方法，很适合于复杂系统的模糊预测和控制。详细地对输入量的选择和学习算法进行了分析。实例表明，此方法具有町靠、鲁棒性好和快速等特点，优于神经网络电力负荷预报方法。     

Time series forecasting based on wavelet decomposition and feature extraction

Time series forecasting is one of the most important issues in numerous applications in real life. The objective of this study was to propose a hybrid neural network model based on wavelet transform (WT) and feature extraction for time series forecasting. The motivation of the proposed model, which is called PCA-WCCNN, is to establish a single simplified model with shorter training time and satisfactory forecasting performance. This model combines the principal component analysis (PCA) and WT with artificial neural networks (ANNs). Given a forecasting sequence, order of the original forecasting model is determined firstly. Secondly, the original time series is decomposed into approximation and detail components by employing WT technique. Then, instead of using all the components as inputs, feature inputs are extracted from all the sub-series obtained from the above step. Finally, based on the extracted features and all the sub-series, a famous neural network construction method called cascade-correlation algorithm is applied to train neural network model to learn the dynamics. As an illustration, the proposed model is compared with two classical models and two hybrid models, respectively. They are the traditional cascade-correlation neural network, back-propagation neural network, wavelet-based cascade-correlation network using all the wavelet components as inputs to establish one model (WCCNN) and wavelet-based cascade-correlation network with combination of each sub-series model (WCCNN multi-models). Results obtained from this study indicate that the proposed method improves the accuracy of ANN and can yield better efficiency than other four neural network models.

     

A new short-term load forecasting method of power system based on EEMD and SS-PSO

Aiming to the disadvantages of short-term load forecasting with empirical mode decomposition (EMD) such as mode mixing and many high-frequency random components, a new short-term load forecasting model based on ensemble empirical mode decomposition (EEMD) and sub-section particle swarm optimization (SS-PSO) is proposed in this paper. Firstly, the load sequence is decomposed into a limited number of intrinsic mode function (IMF) components and one remainder by EEMD, which can avoid the mode mixing problem of traditional EMD. Then, through calculating and observing the spectrum of decomposed series, some low-frequency IMFs are extracted and reconstructed. Other IMFs can be forecasted with appropriate forecasting models. Since IMF1 is main random component of the load sequence, the linear combination model is adopted to forecast IMF1. Because the weights of the linear combination model are very important to obtain high forecasting accuracy, SS-PSO is proposed and used to optimize the linear combination weights. In addition, the factors such as temperature and weekday are taken into consideration for short-term load forecasting. Simulation results show that accuracy of the load forecasting model proposed in the paper is higher than that of BP neural network, RBF neural network, support vector machine, EMD and their combinations.     

基于改进LMD与GRU网络的短期燃气负荷预测

针对燃气负荷数据非线性、非平稳性的特点,本文提出一种基于改进的LMD算法与GRU神经网络的组合预测模型.模型首先利用改进后的LMD算法对燃气负荷数据进行序列分解,改进的LMD方法采用分段牛顿插值法代替传统的滑动平均值法来获得局部均值函数和包络估计函数,改善了传统LMD方法存在的过平滑问题.之后,再将得到的若干PF分量进行小波阈值去噪处理,获得有效的分量数据.最后,利用GRU神经网络分别预测各分量值,将它们相加得到最终的负荷预测值.仿真实验表明,提出的方法与单个GRU神经网络以及结合传统LMD算法的GRU网络相比,预测精度更高.     

基于VMD与MOGOA-LSTM的短期负荷预测模型

为了提高短期负荷预测的准确度,降低非平稳信号对模型预测造成的影响,提出一种集数据挖掘和多目标优化网络于一体的短期负荷预测模型。该方法将电力负荷数据通过变分模态分解技术分解成若干个不同频率的模态分量,采用相间重构方法动态确定人工神经网络的训练测试比和神经元设置,采用长短期记忆神经网络对各分量进行模型搭建,并在长短期记忆神经网络的基础上加入多目标蝗虫智能优化算法来优化网络内部参数,累加所有分量模型预测的值,实现短期负荷预测。仿真结果表明,与统计学方和混合模型相比,本文提出的模型在短期预测方面的预测精度较高、泛化能力更强。     

混合属性约简方法在电力负荷预测中的应用

影响电力短期负荷预测精度的因素众多,为了找到负荷值与各种外在因素之间的关系,提出了一种基于粗糙集理论的混合属性约简算法,并对与预测日相似性数据进行快速约简,讨论了基于混合属性约简和BP神经网络相结合的预测模型。实验结果表明,这种方法提高了短期电力负荷预测精度。     

基于进化神经网络的短期电网负荷预测算法

本文提出了一种基于进化神经网络的短期电网负荷预测算法。该算法使用改进的人工蜂群算法与BP神经网络融合生成进化神经网络,然后使用改进的人工蜂群算法对进化神经网络的偏置和权重进行优化。该算法将火电历史负荷数据作为输入,使用进化神经网络训练预测模型,预测未来一段时间内的电网负荷。首先,获取历史负荷数据。然后,将获取到的数据输入到进化神经网络模型中进行训练。在训练过程中,采用了改进的人工蜂群算法对进化神经网络对神经网络的权重和偏置进行优化,提高模型的预测精度。人工蜂群算法作为一种全局搜索算法,可以有效地探索模型参数空间,找到最优的模型参数组合,从而提高模型的预测精度。为了验证所提出的负荷预测方法的有效性,我们使用了火电网负荷数据进行了测试。实验结果表明本文提出的进化神经网络在短期电网负荷预测方面表现出了良好的预测精度和实用性。与传统的预测方法相比,该算法的预测误差更小,预测结果更加准确可靠。     

A novel method of short-term load forecasting based on multiwavelet transform and multiple neural networks

This paper aims to develop a load forecasting method for short-term load forecasting based on multiwavelet transform and multiple neural networks. Firstly, a variable weight combination load forecasting model for power load is proposed and discussed. Secondly, the training data are extracted from power load data through multiwavelet transform. Lastly, the obtained data are trained through a variable weight combination model. BP network, RBF network and wavelet neural network are adopted as the training network, and the trained data from three neural networks are input to a three-layer feedforward neural network for the load forecasting. Simulation results show that accuracy of the combination load forecasting model proposed in the paper is higher than any one single network model and the combination forecast model of three neural networks without preprocessing method of multiwavelet transform.     

基于外时延反馈人工神经网络的短期负荷预测系统

针对当前人工神经网络学习算法存在的问题,使用变步伐最速下降法和共轭梯度法的混合算法来进行神经网络的训练,并建立了负荷预测的人工神经网络模型。介绍了基于Delphi下的短期电力负荷预测系统。该系统由负荷预测数据查询模块、预测方法模块、结果查询模块和图表输出模块四部分组成。事实说明,混合算法在全局收敛性和收敛速度上要好于传统的算法,所基于此的短期负荷预测系统能达到令人满意的精度。     

Hybrid partial least squares and neural network approach for short-term electrical load forecasting

Intelligent systems and methods such as the neural network (NN) are usually used in electric power systems for short-term electrical load forecasting. However, a vast amount of electrical load data is often redundant, and linearly or nonlinearly correlated with each other. Highly correlated input data can result in erroneous prediction results given out by an NN model. Besides this, the determination of the topological structure of an NN model has always been a problem for designers. This paper presents a new artificial intelligence hybrid procedure for next day electric load forecasting based on partial least squares (PLS) and NN. PLS is used for the compression of data input space, and helps to determine the structure of the NN model. The hybrid PLS-NN model can be used to predict hourly electric load on weekdays and weekends. The advantage of this methodology is that the hybrid model can provide faster convergence and more precise prediction results in comparison with abductive networks algorithm. Extensive testing on the electrical load data of the Puget power utility in the USA confirms the validity of the proposed approach.     

Hybrid partial least squares and neural network approach for short-term electrical load forecasting

Intelligent systems and methods such as the neural network （NN） are usually used in electric power systems for short-term electrical load forecasting. However, a vast amount of electrical load data is often redundant, and linearly or nonlinearly correlated with each other. Highly correlated input data can result in erroneous prediction results given out by an NN model. Besides this, the determination of the topological structure of an NN model has always been a problem for designers. This paper presents a new artificial intelligence hybrid procedure for next day electric load forecasting based on partial least squares （PLS） and NN. PLS is used for the compression of data input space, and helps to determine the structure of the NN model. The hybrid PLS-NN model can be used to predict hourly electric load on weekdays and weekends. The advantage of this methodology is that the hybrid model can provide faster convergence and more precise prediction results in comparison with abductive networks algorithm. Extensive testing on the electrical load data of the Puget power utility in the USA confirms the validity of the proposed approach.