基于小时气象因子的神经网络短期负荷预测模型

目前在短期负荷预测模型中,气象因子的应用主要是其日特征值。负荷对气象因子的响应具有实时性的特点,因此,小时气象因子在负荷预测模型中的应用对提高负荷预测精度具有积极作用。通过分析小时温度、湿度、云量、降水、风等气象因子对电力负荷的影响,并与日气象因子的影响进行对比分析,结果表明:小时气象因子对负荷的影响与日气象因子对负荷的影响特征有很大不同,尤其是在天气发生突然变化时,小时气象因子对电力负荷的影响比日气象因子的影响更加显著。建立了基于小时气象因子的神经网络短期负荷预测模型,预测效果较好。针对目前气象部门对小时气象因子的预测能力及其在实际负荷预测中的应用情况,总结了应用中存在的问题并提出改进策略。     

基于分区逐时气象信息的全网负荷预测研究

负荷与气象是密切相关的,尤其在夏冬两季。以省网负荷预报为例,在传统的电力负荷预测中,所采用的气象信息一般是全省、全天的气象信息,比如全省最高温度、最低温度等等。当前,气象预报技术已经可以做到分区、逐时预报。该文总体思路是将分区逐时气象预测数据应用于负荷预报,以进一步提高负荷预测精度。指出了按照行政区域和气候区域相结合来分区,定义了基于负荷的综合气象因素,提出了根据网供电与综合气象因素进行预测的策略,给出了将相似日方法与支持向量机相结合的负荷预测方法。最后给出在河南电网应用的实际结果,对比表明采用逐时气象信     

基于混合算法的短期负荷预测模糊建模

结合最小二乘（LS）辨识以及一种基于进化规划（EP）和粒子群优化（PSO）的混合进化算法EPPSO,针对对温度比较敏感的夏季负荷,提出一种3阶段短期负荷预测（STLF）算法。在第1阶段,应用LS设计模糊基函数网络（FBFN）完成STLF模糊空间划分;第2阶段,首先拓展FBFN成一阶Sugeno模糊模型,然后应用EPPSO调节其前件参数同时训练后件参数,最后将前述模型用于STLF得出的预测误差看做一个新的时间序列,并仅用气象因素对其进行辨识,可以用回归模型表示该辨识模型,进而应用LS进行辨识。文中提出的STLF模糊建模策略主要贡献于受气象因素影响较大的夏季负荷。仿真部分对浙江省电力公司的实际负荷进行了预测,与其他方法的比较结果证明该方法具有良好的预测性能。     

基于功率谱分解和实时气象因素的短期负荷预测

提出了基于功率谱分解和实时气象因素的短期负荷预测方法,采用快速傅里叶变换(fast Fourier transform,FFT)对负荷序列进行变换得到功率谱,依据变换结果分析功率谱得出负荷基频、低频和高频分量的频率范围,采用有限脉冲响应(finite impulse response,FIR)滤波器从负荷中分离出各 个负荷分量。分析各个负荷分量的特点,针对各个负荷分量分别设计预测模型,对基频分量采用Elman回归神经网络进行预测,这部分较好地反映出基频分量的时间序列特性;对低频和高频分量分别采用自适应线性回归神经网络进行预测,在对这部分分量的预测中重点引入实时气象因素,以利用最新的气象信息提高预测精度。通过在某地区的实际应用证明了所提出方法的有效性。     

Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model

This paper presents a novel time-varying weather and load model for solving the short-term electric load-forecasting problem. The model utilizes moving window of current values of weather data as well as recent past history of load and weather data. The load forecasting is based on state space and Kalman filter approach. Time-varying state space model is used to model the load demand on hourly basis. Kalman filter is used recursively to estimate the optimal load forecast parameters for each hour of the day. The results indicate that the new forecasting model produces robust and accurate load forecasts compared to other approaches. Better results are obtained compared to other techniques published earlier in the literature.     

A neural-net based short term load forecasting using moving window procedure

An improved neural-net approach based on a combined unsupervised/supervised learning concept is proposed. A ‘moving window’ procedure is applied to the most recent load and weather information for creating training set data base. A forecasting lead time that varies from 16 hours to 88 hours is introduced to produce the short term electric load forecasting that meets requirements of real electric utility operating practice. The unsupervised learning (UL) is used to identify days with similar daily load patterns. A feed forward three-layer neural net is designed to predict 24-hour loads within the supervised learning (SL) phase. The effectiveness of proposed methods is demonstrated by comparison of forecasted hourly loads in every single day during 1991 with data realized in the same period in the Electric Power Utility of Serbia (EPS). A better choice of input features and more appropriate training set selection procedure allow significant improvement in forecasting results comparing with our previous UL/SL concept characterized by a fixed neural-net structure and absence of re-training procedure. The improvement is illustrated by reduction of average error in daily energy forecasting for 0.83% and reduction of 90th percentile of 2.04%.     

短期负荷预测中实时气象因素的影响分析及其处理策略

短期负荷预测对于电力系统安全经济运行有着重要的作用,因此,人们一直致力于研究新的预测模型,提高预测精度。目前,实现提高预测精度这个目标的关键是如何更加合理地考虑气象因素对负荷的影响,因为气象敏感负荷在总负荷中所所占的比重越来越大。长期以来,鉴于气象部门无法提供实时温度等气象预测结果,电力系统所建立的预测模型绝大多数都是基于日特征气象因素,诸如日最高温度、最低温度等。针对短期负荷预测,作者剖析了气象因素的影响和作用,分析了处理不同阶段气象因素的策略,并提出了考虑实时气象因素的短期负荷预测新模型,该模型基于神经网络,力图寻求温度、湿度等实时气象因素与负荷曲线之间的相关关系和变化规律。实际应用表明,文中的预测模型和处理策略可以得到更加精确的预测结果。此短期负荷预测新模型也适用于超短期负荷预测。     

基于气象因素粗糙集理论的负荷预测方法

电力负荷受气象因素影响越来越大,如何准确预测负荷中的气象负荷是负荷预测中的一项有意义的课题。本文首先采用粗糙集对影响负荷的气象因素进行规则简约,找到影响负荷的核心气象因素;然后以这些核心因素为坐标寻找与预测日距离最小的历史数据,利用时间序列方法进行预测。经实际系统检验,证明该方法克服了传统气象负荷预测中的主观性,将历史数据的发掘过程量化,便于机器预测。并且预测结果误差小,是一种适用性很强的技术。     

基于改进决策树算法的日特征负荷预测研究

针对决策树ID3算法的缺陷,提出了属性-值对的两次信息增益优化算法,该算法是ID3的改进算法,它能克服ID3算法在选取属性进行扩展时易偏向属性值多的属性及ID3算法属性间相关性考虑较少的缺点;通过对熵阈值的设定,采用预剪枝技术,又能部分克服ID3算法对噪音敏感的不足.该算法可用以生成日特征负荷决策树预测模型.该模型结合预测日的气象、星期等信息,可进行日特征负荷的预测.采用等深直方图分析思想,可对负荷变化率数据离散化,将层次聚类和信息熵相结合,对气象数据离散化.数据预处理后,通过属性-值对的2次信息增益优化算法生成负荷预测决策树模型,在给出预测日气象及星期信息后可对特征负荷进行预测,预测结果能够满足并超过负荷预测实用化标准的要求并具有较高的预测精度.如果将日24点或96点负荷及相应影响因素数据均用该算法进行模型训练,形成24个或96个预测模型,则可进行日24点或96点负荷预测.     

ALFA: automated load forecasting assistant

ALFA, an expert system for forecasting short-term electricity demand is presented. ALFA is in operation at the Energy Management System center at Niagara Mohawk Power Corporation in upstate New York, generating, in real time, hourly load forecasts for up to 48 hours in advance. ALFA uses an extensive 10-year historical database of hourly observations of 12 weather variables and a rule base that takes into account daily, weekly, and seasonal variations of load, as well as holidays, special events, and load growth. A satellite interface for the real-time acquisition of weather data, and the machine-operator interface are also discussed     

An expert system based algorithm for short term load forecast

Existing studies on 1-24 hr load forecasting algorithms are reviewed, and an expert-system-based algorithm is presented as an alternative. The logical and syntactical relationships between weather and load as well as the prevailing daily load shapes have been examined to develop the rules for this approach. Two separate, but similar, algorithms have been developed to provide 1-6 hr and 24 hr forecasts. These forecasts have been compared with observed hourly load data for a Virginia electric utility for all seasons of the year. The 1 hr and 6 hr forecast errors (absolute average) ranged from 0.869% to 1.218% and from 2.437% to 3.48% respectively. The 24 hour forecast errors (absolute average) ranged from 2.429% to 3.300%     

Applying Wavelets to Short-Term Load Forecasting Using PSO-Based Neural Networks

The paper addresses the problem of predicting hourly load demand using adaptive artificial neural networks (ANNs). A particle swarm optimization (PSO) algorithm is employed to adjust the network's weights in the training phase of the ANNs. The advantage of using a PSO algorithm over other conventional training algorithms such as the back-propagation (BP) is that potential solutions will be flown through the problem hyperspace with accelerated movement towards the best solution. Thus the training phase should result in obtaining the weights configuration associated with the minimum output error. Data are wavelet transformed during the preprocessing stage and then inserted into the neural network to extract redundant information from the load curve. This results in better load characterization which creates a more reliable forecasting model. The transformed data of historical load and weather information were trained and tested over various periods of time. The generalized error estimation is done by using the reverse part of the data as a ldquotestrdquo set. The results were compared with traditional BP algorithm and offered a high forecasting precision.     

A real-time short-term load forecasting system using functionallink network

This paper presents a new functional-link network based short-term electric load forecasting system for real-time implementation. The load and weather parameters are modelled as a nonlinear ARMA process and parameters of this model are obtained using the functional approximation capabilities of an auto-enhanced functional link net. The adaptive mechanism with a nonlinear learning rule is used to train the link network on-line. The results indicate that the functional link net based load forecasting system produces robust and more accurate load forecasts in comparison to simple adaptive neural network or statistical based approaches. Testing the algorithm with load and weather data for a period of two years reveals satisfactory performance with mean absolute percentage error (MAPE) mostly less than 2% for a 24-hour ahead forecast and less than 2.5% for a 168-hour ahead forecast     

提高时间序列气象适应性的短期电力负荷预测算法

采用时间序列中的自回归求和移动平均算法(ARIMA)对日负荷进行粗预测,获得消除了周期性的受气象因素影响较强的差值序列。结合气象信息,为小规模神经网络构造能反映气象变化的新息序列,为网络提供良好的训练与适应环境,训出对气象非平稳变化敏感的输出因子Y,再用敏感因子对ARIMA算法的预测结果进行修正,从而构建出对气象适应性较强的ARIMA Y的预测算法。利用Delphi5．0实现的负荷预测软件对广西负荷区进行预测,多年的运行证明：该算法对广西负荷区气象非平稳变化具有很好的敏感性和适应性,能显著提高气象非平稳变化日的预测准确率,较好地解决了在气象变化影响下用ARIMA算法预测准确率偏低的问题。     

Short-term load forecasting based on artificial neural networks parallel implementation

This paper presents the development and application of advanced neural networks to face successfully the problem of the short-term electric load forecasting. Several approaches including Gaussian encoding backpropagation (BP), window random activation, radial basis function networks, real-time recurrent neural networks and their innovative variations are proposed, compared and discussed in this paper. The performance of each presented structure is evaluated by means of an extensive simulation study, using actual hourly load data from the power system of the island of Crete, in Greece. The forecasting error statistical results, corresponding to the minimum and maximum load time-series, indicate that the load forecasting models proposed here provide significantly more accurate forecasts, compared to conventional autoregressive and BP forecasting models. Finally, a parallel processing approach for 24 h ahead forecasting is proposed and applied. According to this procedure, the requested load for each specific hour is forecasted, not only using the load time-series for this specific hour from the previous days, but also using the forecasted load data of the closer previous time steps for the same day. Thus, acceptable accuracy load predictions are obtained without the need of weather data that increase the system complexity, storage requirement and cost.     

面向区域综合能源系统的电-气负荷联合预测研究

能源互联网中电力系统与天然气系统的依赖增强,给综合能源系统中电力系统与天然气系统的负荷预测带来了更高的挑战。文中提出了基于长短记忆网络与权值共享的电-气联合负荷预测方法。文中在预测模型中使用了相关系数对天气因素进行了分析,提取了对两种负荷的重要气象因素,将长短记忆网络作为主要预测算法,权值共享模式分析了电-气两种负荷之间的相关性。算例中使用云南省综合能源系统示范工程数据对算法有效性进行了验证,结果显示该算法有效提高了综合能源系统中电力与天然气负荷预测的精度,有着较高的应用价值。     

大波动地区级系统短期负荷预测方法研究

提出了针对容量较小、负荷波动较大的地区级电力系统适用的短期负荷预测方法,共分为四个步骤：①原始数据的预处理,②用模糊系统预测预测日的峰谷值,③用人工神经网络预测预测日的24h负荷值,④根据第②③步的结果用专家系统决定最终的负荷预测值。利用本文提出的方法对福建永安电力系统1999年1月至2000年6月的数据进行预测,结果证明了方法的快速性和准确性。     

基于RBF神经网络和天气数据的负荷预测虚拟仪器设计

气温、气压等天气因素决定了人体舒适度。随着社会经济的快速发展,空调和取暖负载在总用电负荷中的比重日益增加,天气对负荷波动的影响越来越明显。提出了一种考虑风速、降水、气压、气温、湿度等天气数据的径向基(RBF)神经网络日负荷预测模型,用实际负荷数据和天气数据进行训练,将预测结果与BP网络模型得到的结果进行比较,表明了该模型的优越性,也介绍了基于该模型和LabVIEW、Matlab的负荷预测虚拟仪器的前面板和流程图设计过程。结果表明,提出的模型算法简单、精度高、稳定性好,用虚拟仪器进行电力负荷预测具有操作简单、直观、节省费用等优点。所介绍的方法可以用于其它类型负荷预测模型的虚拟实现。     

电力系统负荷预报的神经网络LBP算法

本文提出了一种能够反映工作日电力负荷波动性并可同时进行假日负荷预报的神经网络算法。该算法在一个神经网络中构造多个相互关联的子网络,将一周７日根据负荷特点分为四类特征日期,通过解码器根据输入的日期特征量激活对应的子网络,对基训练并作出预报。通过对实际系统的实验表明,该算法具有较高的预报精度。     

基于气象成分分解的夏季短期负荷预测

夏季负荷受温度等气象因素影响大,表现出随机性强、波动性大的特点。针对现有短期负荷预测模型在夏季预测精度不高的问题,提出在负荷成分分解的同时,将温度分解为日周期分量和波动分量,以此准确把握短时气象波动对夏季短期负荷预测的影响。在充分分析负荷各分量变化趋势及对整体负荷预测精度影响的基础上,针对各个负荷分量特征分别选择预测方法。在预测气象敏感负荷分量时引入温度波动分量,基于XGBoost智能算法构建预测模型。选用我国中部某市夏季历史负荷建立训练样本,对2017年8月份日96点负荷进行预测,预测结果验证了所提模型和算法的有效性。