短期负荷预测Ｖｏｌｔｅｒｒa滤波器模型

讨论依据相空间邻近轨道演化相似性的特点构造训练模式,建立短期负荷预测Ｖｏｌｔｅｒｒａ滤波器模型的问题．以距离相似度和趋势相似度来衡量电力负荷吸引子轨道的相似度,提出了电力负荷吸引子邻近轨道判别的新方法．从模型训练充分性的角度出发,探讨了模型训练集规模的选择依据．仿真结果表明该模型是有效的．

     

联合灰色模型和神经网络的短期电力负荷预测

电力负荷受温度、湿度等因素影响,因而呈现出典型的非线性和随机性特征,传统单一模型存在预测精度低,泛化能力弱等问题,为此提出一种联合灰色模型(Grey Model,GM)和BP神经网络的组合模型实现对短期电力负荷的高精度预测.GM在小样本、贫信息条件下具有较强的预测能力,但是面对非线性问题时表现不佳,而BP神经网络具有强...     

混合模型神经网络在短期负荷预测中的应用

提出了可应用于电力系统负荷预测的混合模型神经网络方法,该方法同时具有电力系统负荷预测的传统方法的优点及人工视网络方法的优点,该方法中,不同的负荷分量采用不同类型的预测方法,并采用基本绵谐振分量作神经网络的输入,神经网络的训练采用快速的学习算法进行,该方法具有很强的实时性和适应性,适用于没有气象资料的应用场合,仿真计算的结果表明,预测精度较传统来得高。     

电力短期负荷预测模型与软件开发

本文应用模糊理论,人工神经网络等智能技术,确定了有效的电力负荷短期预测算法,其中着重考虑了天气因素对电网负荷的影响,并开发了实用化的负荷在线预测软件,该软件是基于Windows的应用程序的,具有开放式的结构和友好的人机接口。可用于每小时或每15分钟的负荷预测,测试结果表明,该方法具有良好的预测精度。     

递归神经网络及其在电力短期负荷预测中的应用

本文用递归神经网络逼近非线性ＡＲＭＡ模型预测电力短期负荷。与传统方法以及前馈神经网格方法比较，递归神经网络由于其能自学习逼近非线性ＡＲＭＡ模型而具有较高的预测精度，预测方法也比较简单。这在我国电力供应紧张的情况下，对提高我国的电力负荷预测水平，合理安排电力生产计划具有一定的现实意义。     

基于BP神经网络电力负荷短期预测研究

电力负荷预测是电力系统一项重要的工作。本文应用了BP神经网络对南方某地区短期电力负荷进行了预测。首先介绍了BP神经网络结构,其次是利用BP神经网络结合南方某地区电力负荷数据进行实证研究,并且在设计BP神经网络结构时考虑了气象因素对负荷的影响。     

基于神经网络优化组合的短期负荷预测

通过对电力负荷变化规律和影响因素的分析,集结多种单个模型所包含的信息,进行最佳组合,提出了在单一模型预测结果基础之上的基于神经网络的优化组合预测,确定了网络训练样本和隐含层的个数,可使提前一天的预测精度较传统预测模型有较大提高。并当发现某一点预测误差过大,可对该点利用文中提出的误差灰色模型修正预测结果,这样不仅可提高整体预测精度,更重要的是减小最大预测误差值和减少大预测误差发生的次数。仿真结果验证了该预测模型的可行性和有效性。     

模糊神经网络在电力市场短期负荷预测中的应用

在电力市场下的短期负荷预测问题中，考虑到天气和其它影响因素，将模糊系统与神经网络在负荷预测系统中按串联方式连接组成模糊神经网络（FNN），进行短期负荷预测。在负荷预测过程中，首先对负荷、天气、日期等数据进行模糊化处理，然后采用两种典型有效的改进BP算法对神经网络进行训练，并比较其速度与精度等。最后用训练好的网络进行预测，将预测结果与实际数据进行比较，证明其具有较好的预测效果。     

电力系统短期电力负荷预测仿真研究

在短期电力负荷准确预测的研究中,由于模型存在随机、时变、非线性等特点,用传统的神经网络进行预测结果往往不是很理想.为了提高预测的准确率,在建模之前,先通过相似日法选出与预测日总体相似度较高的负荷数据作为输入数据,提高待预测数据与训练数据之间的相关性.然后通过动态模糊神经网络来进行预测.减小建模的复杂性并提升建模速度.最后用EUNITE提供的电力负荷数据来进行仿真,仿真表明相似日和动态模糊神经网络结合的方法预测结果较为理想,提高了预测的准确率.     

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 methodology for short-term electric load forecasting based on specialized recursive digital filters

In this paper we propose a methodology for short-term electric load forecasting, which is adaptive and based on signal processing theory. The main interest here is to construct a next day predictor for the peak and hourly load. To this end the load data are organized into profiles according to day type and temperature interval. For each load profile, we use a specialized adaptive recursive digital filter, for which parameters are estimated on-line by using a recursive algorithm. As a result, the complete forecasting system is nonlinear and the prediction is computed based on the type and on the temperature interval of the next day. The effectiveness of the proposed methodology is illustrated by a numerical example, in which we compare performance of the proposed approach to a non-specialized and a naïve predictors, by using the Mean Absolute Percentage Error (MAPE) of the forecasting errors.     

A hierarchical neural model in short-term load forecasting

This paper proposes a novel neural model to the problem of short-term load forecasting (STLF). The neural model is made up of two self-organizing map (SOM) nets—one on top of the other. It has been successfully applied to domains in which the context information given by former events plays a primary role. The model was trained on load data extracted from a Brazilian electric utility, and compared to a multilayer perceptron (MLP) load forecaster. It was required to predict once every hour the electric load during the next 24 h. The paper presents the results, the conclusions, and points out some directions for future work.     

Short-term power load forecasting using grey correlation contest modeling

Power load has the characteristic of nonlinear fluctuation and random growth. Aiming at the drawback that the forecasting accuracy of general GM(1,1) model goes down when there is a greater load mutation, this paper proposes a new grey model with grey correlation contest for short-term power load forecasting. In order to cover the impact of various certain and uncertain factors in climate and society on the model as fully as possible, original series are selected from different viewpoints to construct different forecasting strategies. By making full use of the characteristic that GM(1,1) model can give a perfect forecasting result in the smooth rise and drop phase of power load, and the feature that there are several peaks and valleys within daily power load, the predicted day is divided into several smooth segments for separate forecasting. Finally, the different forecasting strategies are implemented respectively in the different segments through grey correlation contest, so as to avoid the error amplification resulted from the improper choice of initial condition. A practical application verifies that, compared with the existing grey forecasting models, the proposed model is a stable and feasible forecasting model with a higher forecasting accuracy.     

基于包容性检验和证据理论的短期负荷预测

为了提高短期负荷的预测精度,提出一种包容性检验和证据理论的短期负荷组合预测模型(ET-DS).分别采用多个单一模型对短期负荷进行预测,采用包容性检验选择最合适的单一模型,利用证据理论获取单一模型的权值,实现短期负荷的组合预测,并采用短期负荷数据对模型性能进行仿真测试.仿真结果表明,相对单一模型及其它组合模型,ET-DS组合模型更加准确刻画了短期负荷变化趋势,提高了短期负荷预测精度,预测结果可为电力规划提供有价值参考意见.     

基于相似日负荷修正算法的短期负荷预测

为实现准确、快速预测电力系统短期负荷的目的,综合考虑气象、日类型和时间对负荷的影响,提出了基于相似日负荷修正算法的预测模型.首先建立相似度量化模型,具体用灰色关联分析法计算气象相似度,兼顾"近大远小"和"周期性"原则来量化时间相似度,二者乘积作为总体相似度,依此选取若干相似日;然后基于"日类型"和"时间跨度"修正相似日负荷;最后用加权平均法预测负荷.短期负荷预测的实例结果表明了该算法的可行性.     

A SOM-based hybrid linear-neural model for short-term load forecasting

In this paper, a short-term load forecasting method is considered, which is based upon a flexible smooth transition autoregressive (STAR) model. The described model is a linear model with time varying coefficients, which are the outputs of a single hidden layer feedforward neural network. The hidden layer is responsible for partitioning the input space into multiple sub-spaces through multivariate thresholds and smooth transition between the sub-spaces. In this paper, we propose a new method to smartly initialize the weights of the hidden layer of the neural network before its training. A self-organizing map (SOM) network is applied to split the historical data dynamics into clusters, and the Ho-Kashyap algorithm is then used to obtain the separating planes' equations. Applied to the electricity markets, the proposed method is better able to model the smooth transitions between the different regimes, which are present in the load demand series because of market effects and season effects. We use data from three electricity markets to compare the prediction accuracy of the proposed method with traditional benchmarks and other recent models, and find our results to be competitive.     

A novel combined model based on hybrid optimization algorithm for electrical load forecasting

Accurate electrical load forecasting always plays a vital role in power system administration and energy dispatch, which are the foundation of the smooth operation of the national economy and people’s daily life. Thinking from this vision, many scholars have made great efforts to seek suitable optimization algorithms to improve the performance of existing forecasting algorithm. However, most of the studies ignore the inherent disadvantages of single optimization algorithm, which leads to sub-optimal forecasting performance. Therefore, a novel electric load forecasting system was successfully proposed in this paper by the combination of data preprocessing, hybrid optimization algorithms, and several single classical forecasting methods, which successfully overcomes the defects of single traditional forecasting models and achieves higher forecasting accuracy than that of single model optimization. Besides, the 30 min interval data of Queensland, Australia from March to April is used as illustrative examples to evaluate the performance of the developed model. The results of tests demonstrate that the proposed hybrid model can better approximate the actual value, and it can also be employed as a useful tool for smart grids dispatching planning.     

基于自适应卡尔曼滤波在气象影响下负荷预测

如今电网系统中所构成电力负荷的电器越来越多,其中像空调等受气象影响的负荷所占比例持续升高,那么气象因素（温度、湿度、降雨量等）对电网的影响自然越来越突出,因此短期负荷预测将气象因素考虑进去,能够大大提升预测精度。根据某地区六年的电力负荷数据,构建卡尔曼滤波模型,可以给出高效准确的预测结果。然后将气象因素考虑到自适应卡尔曼滤波模型,通过不断对状态估计进行修正,得到计及气象因素影响的负荷预测结果精度更高。通过MATLAB 仿真,说明这种算法比较传统的卡尔曼滤波具有更高的预测精度,而且这种改进后的算法对实现短期负荷预测提供了一条新的途径。     

基于混沌的交通流量Volterra自适应预测模型*

采用基于混沌动力系统的相空间重构和非线性系统的Volterra级数展开式,构建了交通流量的Volterra自适应预测模型。其基本思想是首先采用Lyapunov指数判定交通流时间序列存在混沌的前提下,对该时间序列进行相空间重构;然后选择Volterra级数构造非线性预测模型,并采用LMS类型的自适应算法来实时调整模型的系数。应用该模型对Lorenz、Rossler和交通流时间序列进行仿真研究。结果表明,提出的Volterra自适应预测模型能有效地预测低维混沌时间序列和交通流时间序列。