Neural network based short-term load forecasting using weathercompensation

This paper presents a novel technique for electric load forecasting based on neural weather compensation. Our proposed method is a nonlinear generalization of Box and Jenkins approach for nonstationary time-series prediction. A weather compensation neural network is implemented for one-day ahead electric load forecasting. Our weather compensation neural network can accurately predict the change of actual electric load consumption from the previous day. The results, based on Hong Kong Island historical load demand, indicate that this methodology is capable of providing a more accurate load forecast with a 0.9% reduction in forecast error     

Weather sensitive short-term load forecasting using nonfullyconnected artificial neural network

The authors present an artificial neural network (ANN) model for forecasting weather-sensitive loads. The proposed model is capable of forecasting the hourly loads for an entire week. The model is not fully connected; hence, it has a shorter training time than the fully connected ANN. The proposed model can differentiate between the weekday loads and the weekend loads. The results indicate that this model can achieve greater forecasting accuracy than the traditional statistical model. This ANN model has been implemented on real load data. The average percentage peak error for the test cases was 1.12%     

电力系统短期负荷预测的模糊神经网络方法

针对电力系统短期负荷预测问题 ,考虑气象因素对负荷的影响 ,提出了一种模糊神经网络的短期负荷预测方法 ,首先根据评价函数选取相似日学习样本 ,然后利用隶属函数对影响负荷的特征因素向量的分量进行模糊处理 ,采用反向传播算法 ,对 2 4点每点建立一个预测模型 ,提高了学习效能。本方法适合在短期负荷预测中使用 ,具有较好的预测精度。     

电力系统短期负荷预测的模糊神经网络方法

针对电力系统短期负荷预测问题,考虑气象因素对负荷的影响,提出了一种模糊神经网络的短期负荷预测方法,首先根据评价函数选取相似日学习样本,然后利用隶属函数对影响负荷的特征因素向量的分量进行模糊处理,采用反向传播算法,对24点每点建立一个预测模型,提高了学习效能.本方法适合在短期负荷预测中使用,具有较好的预测精度.     

A hybrid economic indices based short-term load forecasting system

The bankruptcy event of Lehman Brothers and the corresponding global economic recession in 2008 and 2009, influenced the electricity load demand patterns for which traditional load forecasting approaches were not able to effectively predict. To overcome this problem, this paper proposes a new hybrid economic indices based short-term load forecasting (HEI-STLF) system. In which business indicators, such as the leading index or the coincide index, each combined with stock index as hybrid economic indices influencing factors for the support vector regression (SVR) model, to respond to the economic dynamics and reduce its impact on forecasting accuracy. The Taiwan island-wide electricity load demands from 2008 to 2011 are used as the case study for performance testing with different combinations of the Taiwan business indicator and the Taiwan Stock Exchange Capitalization-Weighted Stock Index (TAIEX). The results show that the proposed HEI-STLF system with hybrid economic indices of an annualized six-month rate of change of composite leading index and a 90 days moving average of TAIEX, achieves the best forecasting performance. Compared to the traditional SVR load forecasting approach, it improves the forecasting accuracy in the best condition by 30.39% in the period when the load demands are affected by the global economic recession.     

电力系统短期负荷预测的级联网络模型研究

提出一种基于BP子网络和小波网络的短期负荷预测的级联网络模型.在对气象影响因素与负荷关系深入分析的基础上,采用BP子网络来映射气象等不确定因素的影响.采用小波网络(预测网络)来映射历史负荷值的影响,它结合了小波变换良好的时频局域化性质和神经网络的自学习能力,明显地改善了神经网络难以合理确定网络结构和存在局部最优等缺陷.最后两级网络相互级联组成预测网络.研究算例表明,这种模型是优秀的.     

电力系统短期负荷预测的级联网络模型研究

提出一种基于BP子网络和小波网络的短期负荷预测的级联网络模型。在对气象影响因素与负荷关系深入分析的基础上,采用BP子网络来映射气象等不确定因素的影响。采用小波网络(预测网络)来映射历史负荷值的影响,它结合了小波变换良好的时频局域化性质和神经网络的自学习能力,明显地改善了神经网络难以合理确定网络结构和存在局部最优等缺陷。最后两级网络相互级联组成预测网络。研究算例表明,这种模型是优秀的。     

A neural network operator oriented short-term and online load forecasting environment

This paper owes its origins to a project, still in progress at ENEL/ARC, which aims to investigate the application of artificial intelligence techniques and eventually to check their positive contribution in the field of short-term load forecasting. In particular, this article focuses on the construction problems of an integrated tool specifically designed to meet the needs of utility forecasters and power system operators. Even if the use of artificial neural networks for short-term forecasting had already been stressed in the past and no longer represents an innovative solution, the authors believe that their use for online forecasting in an adaptive and reliable calculation structure still represents a new subject of interest.     

A neural network based technique for short-term forecasting ofanomalous load periods

The paper illustrates a part of the research activity conducted by the authors in the field of electric short term load forecasting (STLF) based on artificial neural network (ANN) architectures. Previous experiences with basic ANN architectures have shown that, even though these architectures provide results comparable with those obtained by human operators for most normal days, they evidence some accuracy deficiencies when applied to “anomalous” load conditions occurring during holidays and long weekends. For these periods a specific procedure based upon a combined (unsupervised/supervised) approach has been proposed. The unsupervised stage provides a preventive classification of the historical load data by means of a Kohonen's self-organizing map (SOM). The supervised stage, performing the proper forecasting activity, is obtained by using a multi-layer perceptron with a backpropagation learning algorithm similar to the ones mentioned above. The unconventional use of information deriving from the classification stage permits the proposed procedure to obtain a relevant enhancement of the forecast accuracy for anomalous load situations     

一种基于HHT的电力系统短期负荷预测模型

提出了一种基于HHT的电力系统短期负荷预测模型.针对EMD分解电力负荷时存在模态混叠及对高频IMF预测不准确的问题,采用一阶差分算法对EMD分解进行改进,得到消除模态混叠后的一系列IMF分量及余项.通过对各分量的频谱计算和观察,提取出低频分量,并将其进行重构,各分量选取合适模型进行预测.由于IMF1主要为负荷的随机分量,对其考虑天气、节假日因素,并采用粒子群算法对组合权值进行优化.仿真结果表明此种方法具有较高的预测精度.     

短期负荷预测神经网络方法比较

以某地区购网有功功率的负荷数据为背景,建立了三个BP神经网络负荷预测模型--SDBP、LMBP及BRBP模型进行短期负荷预测工作,并对其结果进行比较.针对传统的BP算法具有训练速度慢,易陷入局部最小点的缺点,采用具有较快收敛速度及稳定性的L-M优化算法进行预测,使平均相对误差有了很大改善,具有良好的应用前景.而采用贝叶斯正则化算法可以解决网络过度拟合,提高网络的推广能力,使平均相对误差和每日峰值相对误差降低,但收敛速度过慢(慢于SDBP模型),不适于在实际应用中采用.     

短期负荷预测神经网络方法比较

以某地区购网有功功率的负荷数据为背景,建立了三个BP神经网络负荷预测模型——SDBP、LMBP及BRBP模型进行短期负荷预测工作,并对其结果进行比较。针对传统的BP算法具有训练速度慢,易陷入局部最小点的缺点,采用具有较快收敛速度及稳定性的L-M优化算法进行预测,使平均相对误差有了很大改善,具有良好的应用前景。而采用贝叶斯正则化算法可以解决网络过度拟合,提高网络的推广能力,使平均相对误差和每日峰值相对误差降低,但收敛速度过慢(慢于SDBP模型),不适于在实际应用中采用。     

一种基于HHT的电力系统短期负荷预测模型

提出了一种基于HHT的电力系统短期负荷预测模型。针对EMD分解电力负荷时存在模态混叠及对高频IMF预测不准确的问题,采用一阶差分算法对EMD分解进行改进,得到消除模态混叠后的一系列IMF分量及余项。通过对各分量的频谱计算和观察,提取出低频分量,并将其进行重构,各分量选取合适模型进行预测。由于IMF1主要为负荷的随机分量,对其考虑天气、节假日因素,并采用粒子群算法对组合权值进行优化。仿真结果表明此种方法具有较高的预测精度。     

UD-IGA网络构造法在短期负荷预测中的应用

电力系统短期负荷预测既是电力系统调度部门制定发电计划的依据,也是制定电力市场交易计划的基础,它对电力系统的运行、控制和计划都有着非常重要的影响.可由于负荷预测的复杂性、不确定性,难以获得精确的预测值.为提高预测精度,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,将基于均匀设计(UD)和改进遗传算法(IGA)的网络构造法用于短期负荷预测.数据样本训练和实际预测结果表明,该模型不仅可避免陷入局部极小点,而且提高了预测精度和网络的训练速度.     

UD-IGA网络构造法在短期负荷预测中的应用

电力系统短期负荷预测既是电力系统调度部门制定发电计划的依据,也是制定电力市场交易计划的基础，它对电力系统的运行、控制和计划都有着非常重要的影响。可由于负荷预测的复杂性、不确定性，难以获得精确的预测值。为提高预测精度，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，将基于均匀设计（UD）和改进遗传算法(IGA)的网络构造法用于短期负荷预测。数据样本训练和实际预测结果表明，该模型不仅可避免陷入局部极小点,而且提高了预测精度和网络的训练速度。     

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。     

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     

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 2 4h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 2 0 0 0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。     

基于经验模态分解与动态神经网络的短期负荷预测

提出了采用经验模态分解(EMD)、动态神经网络与BP型神经网络相结合的混合模型进行电力系统短期负荷预测的方法。首先运用EMD将非平稳的负荷序列分解,然后根据分解后各分量的特点构造不同的动态神经网络对各分量分别进行预测,最后对各分量预测结果采用BP网络进行重构得到最终预测结果。仿真结果表明基于该方法的电力系统短期负荷预测具有较高的精度。     

粒子群优化的神经网络模型在短期负荷预测中的应用

为了提高电力系统短期负荷预测精度,针对传统径向基函数(RBF)神经网络在负荷预测中存在的问题,提出一种新的预测模型:粒子群优化的RBF神经网络模型.粒子群算法是一种新的全局优化算法,有很强的全局寻优能力,用它来优化RBF神经网络的权值,并用优化好的RBF网络进行负荷预测.仿真在虚拟仪器LabVIEW和Matlab软件平台上进行,结果表明该预测模型精度高于传统RBF神经网络模型,具有一定实用性.