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。     

电力系统短期负荷预测方法研究综述

根据负荷预测基本流程,分别对数据预处理、模型选取、模型优化分别进行了总结分析。首先对传统的数据处理方法进行了概述,并简要介绍了新的数据处理方法。其次,将现有的短期负荷预测方法分为经典方法、传统方法和智能方法,综合分析了现有预测方法的应用原理,详细分析和比较预测方法的优点和不足之处,为了提高预测的精度,一些新的方法就因运而生,目的在于提高预测精度和适应相应各种运行条件。再次,总结分析了传统的预测优化模型,并简要介绍了现有的一些新的优化模型,这些新的优化模型计算结果相比于传统的模型精确度较高,分析了新优化模型的优点和不足之处。文章最后对了未来电力系统负荷预测提出了展望,在进行短期负荷预测时应该考虑电力市场、新能源、电动汽车相关因素的影响。     

模糊神经网络在电力短期负荷预测中的应用

提出用于电力短期负荷预测(SILF)的一种模糊神经网络(FNN)方法，该方法针对BP网络收敛速度慢、易导致局部极小值的缺点，将考虑气候、温度、星期类型等影响因素的模糊技术与快速二阶BP网络相结合，并以南方电网负荷预测为例，应用MATLAB蚀语言对系统进行仿真训练，测试结果表明，该方法具有较高的预测精度。     

基于QPSO-WNN和模糊理论的电力系统短期负荷预测方法

电力系统的短期负荷预测是电力系统管理的一项重要方法,准确的负荷预测可以保证用户得到安全、经济的供电.针对负荷预测方法的多样性,在小波神经网络用于负荷预测的基础上,提出基于量子粒子群优化算法( QPSO)优化神经网络权值,并运用模糊理论进行修正.某电网负荷的拟合数据表明QPSO优化算法训练的神经网络不仅收敛速度明显加快,而且其预报精度也比较高.     

模糊神经网络在非线性短期负荷预测中的应用

为提高负荷预测精度,提出了一种新的4层模糊神经网络短期负荷预测模型.该模型将模糊逻辑和神经网络的长处融合在一起,使模糊推理和解模糊均通过神经网络来实现.选取的隶属函数使神经网络权值有一定的知识表示意义,并通过模糊化层将输入特征量转化为模糊量.在模糊推理层提出了两种不同的算法来完成模糊推理,然后从中确定出模糊取小算法预测效果更好.最后在输出层通过适当的解模糊得到确切的预测输出值.仿真结果表明了该方法的有效性.     

一种新的电力系统短期负荷预测方法

针对电力系统短期负荷特性,提出了基于局部线性嵌入（Linear Local Embed,LLE）和支持向量机（Support Vector Machine,SVM）技术的短期负荷预测模型。该模型利用LLE算法对负荷样本的数据挖掘知识,得到了高维输入样本的低维映射,最后利用具有非线性拟合、泛化能力强的SVM进行回归。     

基于模糊逻辑和神经网络的短期电力负荷预测

本文分析了天气和节假日对电力负荷的影响 ,建立了神经网络和模糊逻辑相结合的综合预测模型进行短期负荷预测。预测结果经两步得出 ,首先训练神经网络 ,令其预测基本日负荷曲线 ,然后利用模糊逻辑根据天气因素以及是否节假日等情况对负荷曲线进行修正 ,使其在天气突变等情况下也能达到较高的预测精度。采用此模型对石家庄电力系统负荷进行预测分析 ,取得了令人满意的结果。     

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.     

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。     

基于模糊支持向量核回归方法的短期峰值负荷预测

分析了电力系统负荷预测目前采用的方法的不足;在已有研究成果的基础上,根据电网负荷的特点进一步完善了基于模糊支持向量的核回归方法;与目前已有的方法,如神经网络、卡尔曼滤波、最小绝对值参数估计、结合遗传算法的支持向量机、结合模糊小波技术的支持向量机等进行对比实验,实验结果展示了几种方法的性能对比,为该领域的研究提供了参考．     

Short term load forecasting using daily updated load models

This paper proposes one-day-ahead load forecasting using daily updated weekday load models and weekly updated bias models for everyday-of-the-week loads. The load characteristics are examined first for actual data from Kyushu Electric Power Company and weather stations in Kyushu throughout 1982. Then, according to properties of the loads, the algorithm of the load forecasting is derived. Features of the load forecasting are summarized as follows: (1) according to load curve properties, 24 hourly weekday load models are constructed individually during a 24-h period; (2) the weekday load models, which give estimates for the regression coefficients of weather and other factors, are updated everyday by the exponential weighted least squares method (equivalent to the steady state Kalman filter); (3) by using the bias models, the load forecasts for Saturday, Sunday and Monday patterns are also obtained by the same method used as for the weekday pattern. Based on actual data, the accuracy of the proposed load forecasting was found to be very high, the standard deviation of the relative error of the load forecast being about 3%.     

A hybrid system for forecasting 24-h power load profile for Polish electric grid

We present a method of forecasting 24-h power load profile in state-wide power system in Poland. The presented method is based on a hybrid artificial intelligence system. It employs actual temperature forecasts prepared by Interdisciplinary Centre for Mathematical and Computational Modelling of Warsaw University. The machine learning part of the system consists of 24 instances of Hierarchical Estimator: a machine learning method that divides the problem into non-exclusive subproblems with the help of fuzzy clustering and combines results of fairly simple neural networks trained on those subproblems into one, possibly more accurate solution. The presented system also includes a part responsible for dealing with days that have distinct power load patterns, such as additional state holidays. That latter part uses 30 (or 33) appropriately arranged linear regressions.The proposed approach was tested on historical load data from Poland and a few other countries. The achieved MAPE varied from 1.08% to 2.26% in dependence on the country. Such errors are among the lowest achieved by the published methods.     

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.     

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。     

Implementation of hybrid short-term load forecasting system with analysis of temperature sensitivities

Load forecasting is necessary for economic generation of power, economic allocation between plants (unit commitment scheduling), maintenance scheduling, and for system security such as peak load shaving by power interchange with interconnected utilities. A novel hybrid load forecasting algorithm, which combines the fuzzy support vector regression method and the linear extrapolation based on similar days method with the analysis of temperature sensitivities is presented in this paper. The fuzzy support vector regression method is used to consider the lower load-demands in weekends and Monday than on other weekdays. The normal load in weekdays is forecasted by the linear extrapolation based on similar days method. Moreover, the temperature sensitivities are used to improve the accuracy of the load forecasting in relation to the daily load and temperature. The result demonstrated the accuracy of the proposed load forecasting scheme.     

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.     

一种整合多种计算方法的短期负荷预测模型

介绍了一种整合神经网络、专家系统和动态聚类多种智能方法为一体的短期／超短期预测模型,综合考虑了气象、节假日等负荷影响因素。     

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.     

Automatic time series analysis for electric load forecasting via support vector regression

In this work, a strategy for automatic lag selection in time series analysis is proposed. The method extends the ideas of feature selection with support vector regression, a powerful machine learning tool that can identify nonlinear patterns effectively thanks to the introduction of a kernel function. The proposed approach follows a backward variable elimination procedure based on gradient descent optimisation, iteratively adjusting the widths of an anisotropic Gaussian kernel. Experiments on four electricity demand forecasting datasets demonstrate the virtues of the proposed approach in terms of predictive performance and correct identification of relevant lags and seasonal patterns, compared to well-known strategies for time series analysis designed for energy load forecasting and state-of-the-art strategies for automatic model selection.     

最大Lyapunov指数短期电力负荷预测模型仿真

已有的研究工作表明,针对Lyapunov指数预报模式的预测时限受负荷吸引子最大Lyapunov指数的限制,已提出的k△t间隔采样混沌模型在短期电力负荷预测中能有效的提高负荷预测精度,增加预测时限.对k-△t间隔采样混沌模型中求解最大Lyapunov指数的方法进行了改进,对小数据量法产生的数据,引入数据间隔差方法求出最佳拟和数据段.利用VC6.0设计了仿真软件,对某实际电网进行了短期负荷预测,试验结果表明,能有效提高负荷预测精度.