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。     

Short-term electric power load forecasting using feedforward neural networks

Abstract: This paper presents the results of a study on short‐term electric power load forecasting based on feedforward neural networks. The study investigates the design components that are critical in power load forecasting, which include the selection of the inputs and outputs from the data, the formation of the training and the testing sets, and the performance of the neural network models trained to forecast power load for the next hour and the next day. The experiments are used to identify the combination of the most significant parameters that can be used to form the inputs of the neural networks in order to reduce the prediction error. The prediction error is also reduced by predicting the difference between the power load of the next hour (day) and that of the present hour (day). This is a promising alternative to the commonly used approach of predicting the actual power load. The potential of the proposed method is revealed by its comparison with two existing approaches that utilize neural networks for electric power load forecasting.     

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。     

基于XGBOOST-DNN的中期电力负荷预测

精准的负荷预测是电力工作者重要的工作之一,而负荷预测以预测周期的不同,一般可以划分为短期电力负荷预测与中长期电力负荷预测.其中中长期电力负荷预测相较短期电力负荷预测而言,该领域缺乏大量前沿工作者的探索.因此本文提出一种可应用于中期电力负荷预测领域且基于XGBoost-DNN的算法.该算法将树模型和深度神经网络相结合,并将短期电力负荷预测引入到了中期电力负荷预测的工作中,基于树模型自身特点,将数据特征加工成高阶的交叉特征,同时结合原有数据利用深度神经网络可学习到丰富的特征信息.这里是以2017全球能源预测竞赛的数据进行算法分析,其中实验表明,在中期电力负荷预测领域,该方法提出的XGBoost-DNN模型相较于DNN,LSTM而言,其具备更加精准的准确性.     

改进Pi-sigma神经网络及其算法在短期负荷预测中的应用

针对电力负荷的特点,综合考虑历史负荷、天气、日类型等因素的影响,将模糊逻辑和神经网络的长处融合在一起,构建了基于改进Pi-sigma神经网络及其算法的短期负荷预测模型,用于预测预报日的各小时负荷,其中在学习速率的选择、隶属度函数参数的更新等多处进行了改进,进一步减小了预测误差.地区电网的实际应用证明了该算法的有效性.     

ACOA-RBF网络模型在短期负荷预测中的应用

为了进一步提高RBF神经网络的性能,实现准确、快速预测短期电力负荷的目的,将蚁群优化算法（ACOA）作为RBF神经网络的学习算法,建立了一种新的蚁群优化算法的RBF（ACOA-RBF）网络预测模型,利用山西某地区电网的历史数据进行短期负荷预测。仿真表明,这一算法与传统的RBF神经网络预测方法相比,能达到更好的预测效果。该优化算法改善了径向基神经网络的泛化能力,提高了山西电网短期负荷预测的精度,可有效用于电力系统的短期负荷预测。     

基于最大偏差相似性准则的BP神经网络短期电力负荷预测算法

针对企业电力负荷随机性强、稳定性低、预测精度不理想等问题,提出了一种基于最大偏差相似性准则的BP神经网络短期电力负荷预测算法。首先对最大偏差相似性准则算法进行修改,并提出使用预测日的负荷特征向量与最大偏差相似性准则算法聚类之后的类中心负荷特征的距离来确定预测日的相似日类别;然后将聚类后的相似日类别负荷数据作为BP网络的训练数据,输出预测日起始的连续三天96整点负荷值。实验表明,该方法提出的短期电力负荷预测方法在精度和网络训练时间上都有较大的提升,具有较高的有效性和实用性。     

Momentum-based wavelet and double wavelet neural networks for power system applications

In order to minimize the power loss and to control the voltage in the power systems, the proposed momentum-based wavelet neural network and proposed momentum-based double wavelet neural network are proposed in this paper. The training data are obtained by using linear programming method by solving several abnormal conditions. The control variables considered are generator voltages and transformer taps, and the dependent variables are generator reactive powers and load bus voltages. The IEEE 14-bus system and IEEE 30-bus system are tested using the linear programming, Levenberg–Marquardt artificial neural network, proposed momentum-based wavelet neural network and proposed momentum-based double wavelet neural network to validate the effectiveness of the proposed MDWNN method. The trained neural networks are capable of controlling the voltage, and reactive power in power systems is proved by the results with the high level of precision and speed.

     

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.     

采用稳健回声状态网络的超短期负荷预测方法

针对超短期负荷预测周期短,要求预测速度快的特点,构建了基于稳健回归和回声状态网络的超短期负荷预测方法。回声状态网络作为一种递归神经网络,其隐含层为一个储备池,并且通过线性回归训练网络,从而具有映射复杂动态系统的能力和训练快速的特点,能较好地满足超短期负荷预测的要求。考虑到异常负荷数据的影响,将稳健回归运用于网络训练阶段,以削弱异常值的影响,从而提升预测的精度。通过算例验证了所提方法的可行性和有效性。     

基于PCA与改进BP神经网络相结合的电网中长期负荷预测

本文在标准反向传播神经网络的基础上,提出一种结合主成分分析法和改进的误差反向传播神经网络的方法来对电网中长期的电力负荷进行预测。首先利用主成分分析法对电力负荷的影响因素进行特征提取,有效地降低数据样本的维度,消除数据的冗余和线性信息,保留主要成分作为模型的输入数据。然后在标准的神经网络的反向传播环节中引入动量项和陡度因子。两种方法的结合有效地解决了网络收敛速度慢和容易陷入局部最小值的问题。将此方法应用于济源市的中长期电力负荷预测,实验结果表明,基于主成分分析法与改进的反向传播神经网络相结合的方法比常用的标准的反向传播神经网络、基于多变量的时间序列网络及时间序列网络具有更高的计算效率和预测精度,证明提出的预测模型在电力负荷预测中是有效的。     

免疫人工鱼群神经网络的经济预测模型

针对BP神经网络在经济预测存在的问题,提出了一种新的经济预测模型──免疫人工鱼群神经网络（IAFSA-NN）。通过免疫人工鱼群算法（IAFSA）训练神经网络,能显著提高网络的学习精度、收敛速度、泛化能力、还能在一定程度上克服BP神经网络的缺陷。以广东省湛江市的经济数据进行建模,给出了IAFSA训练神经网络的基本原理和步骤,构建了一个免疫人工鱼群神经网络的GDP预测模型,并运用MATLAB7.0进行仿真。实证表明,该模型预测结果优于BP网络预测方法,更接近实际数据,IAFSA神经网络用于经济预测是有效可行的。     

An adaptively trained neural network

A training procedure that adapts the weights of a trained layered perceptron artificial neural network to training data originating from a slowly varying nonstationary process is proposed. The resulting adaptively trained neural network (ATNN), based on nonlinear programming techniques, is shown to adapt to new training data that are in conflict with earlier training data without affecting the neural networks' response to data elsewhere. The adaptive training procedure also allows for new data to be weighted in terms of its significance. The adaptive algorithm is applied to the problem of electric load forecasting and is shown to outperform the conventionally trained layered perceptron.     

短期电力负荷预测的灰色-小波网络组合模型

短期电力负荷数据具有离散、无规则波动的特点,先利用灰色预测弱化其波动性,然后将负荷原始检测数据与其相对应的灰色预测数据进行重构后作为小波网络的训练样本,在此基础上建立基于灰色-小波网络组合模型的短期电力负荷预测新方法。该方法有效整合了灰色理论、小波分析和人工神经网络的优点,与传统BP网络相比,收敛速度更快,预测精度更高。仿真试验表明了该方法用于短期电力负荷预测的可行性和有效性。     

A New Multiplicative Seasonal Neural Network Model Based on Particle Swarm Optimization

In recent years, artificial neural networks (ANNs) have been commonly used for time series forecasting by researchers from various fields. There are some types of ANNs and feed forward neural networks model is one of them. This type has been used to forecast various types of time series in many implementations. In this study, a novel multiplicative seasonal ANN model is proposed to improve forecasting accuracy when time series with both trend and seasonal patterns is forecasted. This neural networks model suggested in this study is the first model proposed in the literature to model time series which contain both trend and seasonal variations. In the proposed approach, the defined neural network model is trained by particle swarm optimization. In the training process, local minimum traps are avoided by using this population based heuristic optimization method. The performance of the proposed approach is examined by using two real seasonal time series. The forecasts obtained from the proposed method are compared to those obtained from other forecasting techniques available in the literature. It is seen that the proposed forecasting model provides high forecasting accuracy.     

An ensemble of neural networks for weather forecasting

This study presents the applicability of an ensemble of artificial neural networks (ANNs) and learning paradigms for weather forecasting in southern Saskatchewan, Canada. The proposed ensemble method for weather forecasting has advantages over other techniques like linear combination. Generally, the output of an ensemble is a weighted sum, which are weight-fixed, with the weights being determined from the training or validation data. In the proposed approach, weights are determined dynamically from the respective certainties of the network outputs. The more certain a network seems to be of its decision, the higher the weight. The proposed ensemble model performance is contrasted with multi-layered perceptron network (MLPN), Elman recurrent neural network (ERNN), radial basis function network (RBFN), Hopfield model (HFM) predictive models and regression techniques. The data of temperature, wind speed and relative humidity are used to train and test the different models. With each model, 24-h-ahead forecasts are made for the winter, spring, summer and fall seasons. Moreover, the performance and reliability of the seven models are then evaluated by a number of statistical measures. Among the direct approaches employed, empirical results indicate that HFM is relatively less accurate and RBFN is relatively more reliable for the weather forecasting problem. In comparison, the ensemble of neural networks produced the most accurate forecasts.     

Application of bacterial foraging technique trained artificial and wavelet neural networks in load forecasting

A new load forecasting (LF) approach using bacterial foraging technique (BFT) trained wavelet neural network (WNN) is proposed in this paper. Artificial neural network (ANN) is combined with wavelet transform called wavelet neural network is applied for LF. The parameters of translation and dilation in the wavelet nodes and the weighting factors in the weighting nodes are tuned using BFT optimization. With the advantages of global search abilities of BFT as well as the multiresolution and localizing natures of wavelets, the networks are constructed which identifies the inherent non-linear characteristics of power system loads. The proposed approach is validated with Tamil Nadu Electricity Board (TNEB) system, India. The comparison of Delta Rule and BFT-based LF for different periods are depicted with their mean absolute percentage errors (MAPE).     

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.

     

深度学习中的权重初始化方法研究

深度神经网络训练的实质是初始化权重不断调整的过程,整个训练过程存在耗费时间长、需要数据量大等问题。大量预训练网络由经过训练的权重数据组成,若能发现预训练网络权重分布规律,利用这些规律来初始化未训练网络,势必会减少网络训练时间。通过对AlexNet、ResNet18网络在ImageNet数据集上的预训练模型权重进行概率分布分析,发现该权重分布具备单侧幂律分布的特征,进而使用双对数拟合的方式进一步验证权重的单侧分布服从截断幂律分布的性质。基于该分布规律,结合防止过拟合的正则化思想提出一种标准化对称幂律分布（NSPL）的初始化方法,并基于AlexNet和ResNet32网络,与He初始化的正态分布、均匀分布两种方法在CIFAR10数据集上进行实验对比,结果表明,NSPL方法收敛速度优于正态分布、均匀分布两种初始化方法,且在ResNet32上取得了更高的精确度。     

Short‐term electric power load forecasting based on cosine radial basis function neural networks: An experimental evaluation

This article presents the results of a study aimed at the development of a system for short‐term electric power load forecasting. This was attempted by training feedforward neural networks (FFNNs) and cosine radial basis function (RBF) neural networks to predict future power demand based on past power load data and weather conditions. This study indicates that both neural network models exhibit comparable performance when tested on the training data but cosine RBF neural networks generalize better since they outperform considerably FFNNs when tested on the testing data. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 591–605, 2005.