Overview and comparison of long-term forecasting techniques for a fast developing utility: part I

The intrinsic uncertainties associated with demand forecasting become more acute when it is required to provide an invaluable dimension to the decision-making process in a period characterized by fast and dynamic changes. In this paper, estimates of the peak demand, pertaining to a typical fast growing system with inherit dynamic load characteristics, and also a normal developing system, is derived from the classical long-term forecasting methods. These demand estimates are compared with corresponding actual values. Then, a proposed model based on demand characteristics of fast developing utility is obtained to yield best fit. Afterwards, improved modeling of the system load characteristics using a knowledge-based expert system, described in a companion paper (Part II), will demonstrate better forecasts compared with forecasts obtained by direct applications of classical techniques.     

Long range peak demand forecasting under conditions of high growth

An extended logistic model with a varying asymptotic upper bound for long-range peak demand forecasting is described. The model has been applied to a typical fast growing system, the Saudi Consolidated Electric Company. The forecasts are compared with actual demands and with those obtained from classical forecasting methods. The model gave relatively accurate peak demand forecasts compared with other classical methods. The model with a single load observation is capable of producing several peak demand forecasts corresponding to different levels of maximum temperature and various levels of social activity. The forecasts produced by the model were also stable irrespective of the length of the ex-post simulation period     

One-hour-ahead load forecasting using neural network

Load forecasting has always been the essential part of an efficient power system planning and operation. Several electric power companies are now forecasting load power based on conventional methods. However, since the relationship between load power and factors influencing load power is nonlinear, it is difficult to identify its nonlinearity by using conventional methods. Most of papers deal with 24-hour-ahead load forecasting or next day peak load forecasting. These methods forecast the demand power by using forecasted temperature as forecast information. But, when the temperature curves changes rapidly on the forecast day, load power changes greatly and forecast error would going to increase. In conventional methods neural networks uses all similar day's data to learn the trend of similarity. However, learning of all similar day's data is very complex, and it does not suit learning of neural network. Therefore, it is necessary to reduce the neural network structure and learning time. To overcome these problems, we propose a one-hour-ahead load forecasting method using the correction of similar day data. In the proposed prediction method, the forecasted load power is obtained by adding a correction to the selected similar day data     

计及需求响应的主动配电网短期负荷预测

随着分布式电源、电动汽车及储能等广义需求响应资源的接入,用户在电力市场各种激励影响下进行需求响应,将改变负荷特性并影响负荷预测。根据需求响应计划信号的可预知性及季节性基础负荷的独立性,利用小波分解等方法对主动配电网负荷在不同层面上进行了分解,形成季节性基础负荷和需求响应信号及各种气象因素作用的负荷部分,利用时间序列模型对季节性基础负荷进行预测,利用支持向量回归模型对需求响应信号及气象因素影响的负荷部分进行预测,形成组合预测模型,两部分预测负荷叠加得到总负荷。利用线性时变模型仿真的主动配电网负荷数据算例,进行了预测测试与分析,通过与其他方法相比较,证明了所提方法预测计及需求响应的主动配电网负荷的有效性及精确度。     

Neural-network-based demand forecasting in a deregulatedenvironment

The traditional approach to load forecasting is based on processing time series of load and weather factors recorded in the past. In the dynamic environment of the deregulated power industry, historical load data may not always be available. This paper explores the possibility of an alternative approach toward load forecasting based on indirect demand estimation from available customer data. This approach requires utilization of demand models for different customer categories. This paper presents a neural network-based method of demand modeling. Neural networks are designed and trained based on the aggregate demands of the groups of surveyed customers of different categories. The performance of such models depends on the neural network design and representativeness of the training data. The forecast accuracy is also affected by the forecasted group size, customer characteristics, customer classification system, and the extent of demand survey. This paper discusses the issues of neural network design and illustrates the proposed method by its application to forecasting demand of residential customers     

聚类分析法在短期负荷预测中的应用

短期负荷预测是指预测未来24 h内的电力负荷需求,这是一项非常重要的工作。目前,负荷预测的实用计算方法有很多,线性回归法、时间序列法、人工神经网络法等等,但是,这些算法的预测精度欠佳。现根据相似日负荷的相似性,提出利用聚类分析法来进行短期负荷预测,为此,介绍聚类分析的步骤、方法及验证算例。实际运行结果表明:利用聚类分析法进行负荷短期预测,短期负荷预测的精度大大提高。     

BP人工神经网络负荷预测模型的L-M训练算法

根据电力系统短期负荷预测的需要,用C 开发了单隐含层BP人工神经网络程序。程序用Levenberg-Marquardt训练算法实现神经网络训练,大大提高了训练速度。采用24个单输出人工神经网络模型分别预测每天的整点负荷。该预测模型可动态生成,提高了预测模型的自适应性。实际算例结果表明,采用该算法及其程序进行短期负荷预测,可得到令人满意的训练速度及预测精度。     

体感温度滚动预报系统及在湖南电网短期负荷预测中的应用

对影响局部地区气温变化的10多个因子作了较为详尽的分析研究,通过对湖南不同地理位置有代表性的气象观测资料的分析对比,找出了湖南不同地点的主要温敏因子,最终建立了湖南14个地市的48 h气温及体感温度滚动预报系统,其产品可及时输入湖南电力短期负荷预测系统,对提高预测准确率起到重要作用。     

地区电网日负荷特性分析及预测方法研究

夏季负荷波动大,预测难度较大。如何提高日负荷预测精度,成为调度部门迫切需要解决的一项重要任务。为有效提高夏季日负荷预测精度,对成都电网日负荷特性进行了深入研究。借鉴电力系统分析中的标幺制理论,将日负荷曲线解析为标幺负荷曲线与最大负荷。针对两者不同的变化特征和影响因素,分别引入与之匹配的自适应指数平滑法和基于模糊聚类的径向基函数(RBF)神经网络法,为负荷预测精度的提高提供了有效的解决途径。实例计算证明,该方法可获得理想的预测效果,能为调度部门提供负荷预测的决策依据。     

电力负荷特性预测软件及其在浙江电网中的应用

介绍了新开发的电力负荷特性预测软件包实现的年持续负荷曲线预测和典型日负荷曲线预测的预测原理和方法.软件既能通过多种负荷预测模型对未来年的年最大负荷、用电量进行预测;也能对未来年的负荷特性曲线进行预测.其中,年持续负荷曲线采用排序法进行预测;典型日负荷曲线采用分行业叠加法进行预测.应用结果表明本软件的预测效果较好,达到了实用化水平.     

基于小波变换和SVM算法的微电网短期负荷预测研究

为了满足微电网的建设和发展对其负荷预测的精度和方法适应性提出的更高要求,本文在时域和频域上比较分析了微电网负荷曲线和传统负荷曲线,得出了微电网负荷波动性更强的结论,然后根据微电网负荷的特点,考虑微电网负荷受星期类型和气象因素的影响,提出对微电网负荷进行离散小波分解的基础上,建立支持向量机(SVM)模型对各层分量分别进行预测,最后运用分解关系得出预测结果。研究表明,与直接应用SVM模型预测相比,分解微电网负荷曲线后再进行SVM模型预测能够实现更高的预测精度,更适用于当前微电网短期负荷预测需要。     

基于组合预测模型的长沙市长期负荷预测

在长沙经济发展预测的基础上，采用回归分析法、趋势外推法、灰色预测方法和弹性系数法等多种方法预测长沙城区2005-2020年的负荷需求，并结合这些方法，提出了基于误差权重因子的多种方法组合预测的思路，对长沙市的长期负荷需求进行了优化组合预测．对预测结果的分析表明，该预测方法是合理、有效的．     

Long-Term Load Forecasting Using Grey System Theory

Supply and demand in power system planning and operation is required to be balanced. An operational reserve for protection against faults or accidental demands also is required. Therefore load forecasting is one of the most important fields and various load forecasting methods have been applied. In this paper the grey system theory which mats uncertain information is applied to the long-term load forecasting from three aspects: the point prediction; the interval prediction; and the topological forecasting. In the point prediction, the annual total demand is predicted, in the interval prediction, the annual peak demand is predicted, and in the topological forecasting, the date where a yearly maximum peak demand would occur is predicted. The grey dynamic model (abbreviated as GM model) is adopted as the predicted model. The GM model is a differential equation model which is different from most forecasting models. The GM model is quite powerful when combined with the preliminary transformation called the accumulated generating operation (AGO). This paper proposes a new method for the long-term load-forecasting problems involving uncertainty. The predicted results have been found to be very satisfactory. The grey system theory is a new tool which is very efficient for load forecasting.     

电力负荷混沌动力特性及其短期预测

根据非线性动力系统理论进行负荷建模和预测,并将预测精度作为一种辨识工具,用于分析电力负荷变化的动力特性。分析结果表明,可将负荷的变化特性描述为低维混沌系统。根据负荷的混沌特性及一步向前预测的精度提出一种优选重构参数的方法,并采用基于相空间重构的加权一阶局域法多步预测模型进行了负荷预测。相空间模型能识别负荷序列的内部特性并进行预测,因此是分析和预测负荷的有效工具。     

基于多指数平滑预测模型的中长期需电量预测

在电力市场运营模式下,对电力负荷的科学、准确预测,对电力系统安全、经济运行,具有十分重要的意义。提出了一种基于多指数平滑模型的中长期需电量预测方法,并应用prony法对历史数据进行分析和处理,得到多指数平滑预测模型中的各个参数,从而预测其后几年的需电量。最后利用该模型,对某个地区的1996～1999年的年需电量进行预测,算例表明,该方法具有较高的预测准确度,是一种行之有效的中长期需电量预测方法。     

基于灰色马尔科夫预测模型的中长期电力负荷预测

中长期负荷预测在电网发展规划编制中占有重要地位,而其关键是数学模型的建立。本文建立了基于马尔科夫修正的灰色负荷预测模型,利用灰色预测模型对未来负荷进行预测,对预测结果采用马尔可夫链预测方法进行改进,提高其预测的准确性。采用灰色马尔科夫模型对某市用电需求负荷建立预测模型,预测了2006至2008年的用电负荷,与实际用电负荷进行对比,结果验证了灰色马尔科夫模型在对电力负荷进行长期预测时具有较高的精度。     

基于支持向量机的中长期日负荷曲线预测

提出了一种预测中长期日负荷曲线的新方法,通过历史典型日负荷数据构造出典型日年度发展时间序列,运用支持向量机方法对预测日各时刻负荷值进行预测并得到了典型日负荷曲线。该方法不需要对日负荷特性、最大负荷及需电量进行预测,因此避免了可能的误差积累问题。以某电网为例对该方法进行了测试,结果表明其具有较高的预测精度。     

Long-term load forecasting and economical operation of wind farms for Egyptian electrical network

Several traditional methods have been presented for long-term load forecasting of electrical power systems without sufficient accuracy of the relevant results. In this paper, in order to improve the results accuracy, the artificial neural network (ANN) technique for long-term peak load forecasting is modified and proposed as an alternative technique in long-term load forecasting. The modified technique is applied on the Egyptian electrical network dependent on its historical data to predict the electrical peak load demand forecasting up to year 2017. This technique is compared with extrapolation of trend curves as a traditional method. Installed power generation capacities of Egyptian electrical network up to year 2017 are estimated dependent on the peak load forecasting of this network. Also, a proposed methodology to assess the economical operation of the wind farms (WFs) beside the conventional power system (CPS) is introduced. This methodology includes a mathematical model to develop the economical operation of wind farms on the whole power generation capacity through a considered period.     

能源互联环境下考虑需求响应的区域电网短期负荷预测

在区域能源互联系统中,需求响应改变了电力用户的常规用电习惯,增加了预测环境的不确定因素。针对这种特定的环境,提出一种考虑需求响应的区域电网短期负荷预测方法来满足企业对预测精度的需求,该方法通过依次构建数据处理模型、负荷预测模型和误差预测模型实现了预测精度的提升。更具体地,针对历史数据样本集,采用灰色关联分析法处理气象数据以获取输入预测模型的相似日特征变量;针对电力负荷预测,建立了长短期记忆网络模型,利用其特殊的门结构选择性地控制输入变量对模型参数的影响,从而改善了模型的预测性能;针对误差数据样本集,采用了动态模式分解技术来挖掘误差数据的潜在价值,并利用其数据驱动特性刻画了误差时间序列的趋势变化特征,实现了良好的误差预测。最后,结合实际的电网数据,对比验证了所提方法的有效性和优越性。     

动态相似与静态相似相结合的短期负荷预测方法

气温对负荷的影响存在累积效应,这导致气象条件与日类型相同两天的负荷仍然可能有较大的差异。因此,现有的以当天气象条件和日类型为特征参量的相似日选取方法不能保证预测的准确率。针对目前累积效应带来的影响,提出了一种动态相似的思路,并且将其与现有的静态相似方法相结合得到一种新的短期负荷预测方法。运用解耦模型分别对待预测日的日平均负荷和负荷曲线形状进行预测。采用动态相似的思路进行日平均负荷预测,采用静态相似日的思路进行负荷曲线形状的预测。算例中,分别以对节假日的负荷预测以及连续高温日期的负荷预测为例,通过对北京某地区的实例计算,结果表明,该预测方法可以提高短期负荷预测的准确率。