Short‐term load forecasting using informative vector machine

In this paper, a novel method is proposed for short‐term load forecasting, which is one of the important tasks in power system operation and planning. The load behavior is so complicated that it is hard to predict the load. The deregulated power market is faced with the new problem of an increase in the degree of uncertainty. Thus, power system operators are concerned with the significant level of load forecasting. Namely, probabilistic load forecasting is required to smooth power system operation and planning. In this paper, an IVM (Informative Vector Machine) based method is proposed for short‐term load forecasting. IVM is one of the kernel machine techniques that are derived from an SVM (Support Vector Machine). The Gaussian process (GP) satisfies the requirements that the prediction results are expressed as a distribution rather than as points. However, it is inclined to be overtrained for noise due to the basis function with N² elements for N data. To overcome this problem, this paper makes use of IVM that selects necessary data for the model approximation with a posteriori distribution of entropy. That has a useful function to suppress the excess training. The proposed method is tested using real data for short‐term load forecasting. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 166(2): 23– 31, 2009; Published online in Wiley InterScience ( www. interscience.wiley.com ). DOI 10.1002/eej.20693     

Risk quantification for ANN based short‐term load forecasting

A new risk assessment method for short‐term load forecasting is proposed. The proposed method makes use of an artificial neural network (ANN) to forecast one‐step‐ahead daily maximum loads and evaluate uncertainty of load forecasting. With ANN as the model, the radial basis function (RBF) network is employed to forecast loads due to its good performance. Sufficient realistic pseudo‐scenarios are required to carry out quantitative risk analysis. The multivariate normal distribution with the correlation between input variables is used to give more realistic results to ANN. In addition, the method of moment matching is used to improve the accuracy of the multivariate normal distribution. The peak over threshold (POT) approach is used to evaluate risk that exceeds the upper bounds of generation capacity. The proposed method is successfully applied to real data of daily maximum load forecasting. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 166(2): 54– 62, 2009; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20464     

Feature extraction of one‐step‐ahead daily maximum load with regression tree

In this paper, a new efficient feature extraction method is proposed to handle the one‐step‐ahead daily maximum load forecasting. In recent years, power systems become more complicated under the deregulated and competitive environment. As a result, it is not easy to understand the cause and effect of short‐term load forecasting with a bunch of data. This paper analyzes load data from the standpoint of data mining. By it we mean a technique that finds out rules or knowledge through large database. As a data mining method for load forecasting, this paper focuses on the regression tree that handles continuous variables and expresses a knowledge rule as if‐then rules. Investigating the variable importance of the regression tree gives information on the transition of the load forecasting models. This paper proposes a feature extraction method for examining the variable importance. The proposed method allows to classify the transition of the variable importance through actual data. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 156(2): 43–51, 2006; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20104     

An Electricity Price Forecasting Model with Fuzzy Clustering Preconditioned ANN

In this paper, a hybrid model of fuzzy clustering and ANN (Artificial Neural Network) is proposed for electricity price forecasting. Due to the complicated behavior of electricity price in power markets, market players are interested in maximizing profits while minimizing risks. As a result, more accurate models are required to deal with electricity price forecasting. This paper proposes a new method that makes use of fuzzy clustering preconditioned GRBFN (Generalized Radial Basis Function Network) to provide more accurate predicted prices. Fuzzy clustering plays a key role to prevent the number of learning data from decreasing at each cluster. GRBFN is one of efficient ANNs to approximate nonlinear systems. Furthermore, a modified GRBFN model is developed to improve the performance of GRBFN with the use of DA (Deterministic Annealing) clustering for the parameters initialization and EPSO (Evolutionary Particle Swarm Optimization) for optimizing the parameters of GRBFN. The proposed method is successfully applied to real data of ISO New England, USA.     

基于神经网络和模糊理论的短期负荷预测

电力系统负荷预测是能量管理系统(EMS)的重要组成部分,它对电力系统的运行、控制和计划都有着非常重要的影响,提高电力系统负荷预测的准确度既能增强电力系统运行的安全性,又能改善电力系统运行的经济性,但负荷预测的复杂性、不确定性使传统的基于解析模型和数值算法的模型难以获得精确的预测负荷。为提高电力系统短期负荷预测准确度,构建了一种新型的负荷预测模型。该模型首先采用多层前馈神经网络,以与预报点负荷相关性最大的几种因素作为输入因子,以改进BP算法作为预测算法,来获得预报日相似日负荷曲线;然后引入自适应模糊神经网络,用于预测预报日的最大、最小负荷;针对模糊神经元的权值更新问题,采用一种新的权值更新算法———一步搜索寻优法,在获得预报日相似日各点负荷和最大、最小负荷的基础上,通过纵向变换,对预报日的负荷修正,进一步减小预测误差。用上述模型和算法预测某地区电网的短期负荷,取得了良好的预测效果。     

应用人工神经网络算法进行短期负荷预测

针对电力负荷预测对Kohonen网的聚类能力和BP网的非线性拟合功能进行了讨论,提出了一种建立负荷日类型模型的方法,并在此基础上用Kohonen网和BP网组合而成的神经网络模型来进行短期负荷预测,提高了负荷预测的精度。     

Short‐term electricity load and price forecasting based on clustering and next symbol prediction

Short‐term electricity load and price forecasting is an important issue in competitive electricity markets. In this paper, we propose a new direct time series forecasting method based on clustering and next symbol prediction. First, the cluster label sequence is obtained from time series clustering. Then a lossless compression algorithm of prediction by partial match version C coder (PPMC) is applied on this obtained discrete cluster label sequence to predict the next cluster label. Finally, the whole time series values of one‐step‐ahead can be directly forecast from the predicted cluster label. The proposed method is evaluated on electricity time series datasets, and the numerical experiments show that the proposed method can achieve promising results in day‐ahead electricity load and price forecasting. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于 Lyapunov 指数的电力系统短期负荷预测

采用非线性系统理论对电力系统历史负荷数据序列进行了特征分析,计算出Ｌｙａｐｕｎｏｖ指数,并利用该Ｌｙａ－ｐｕｎｏｖ指数模式进行短期负荷预测,进而提出短期负荷预测的时间尺度的概念。这种方法不利用气候和气温等数据,只利用电力系统一维峰值负荷历史数据计算出过去的变动模式进行负荷预测,就可以得到较高的预测精度。对东北电网实际负荷数据进行了预测,结果令人满意,从而为电力系统短期负荷预测提供了一种新的预测方法。     

Electricity peak load forecasting with self‐organizing map and support vector regression

This paper aims to study the short‐term peak load forecasting (PLF) by using Kohonen self‐organizing maps (SOM) and support vector regression (SVR). We first adopt a SOM network to cluster the input data set into several subsets in an unsupervised learning strategy. Then, several SVRs for the next day's peak load are used to fit the training data of each subset in the second stage. In the numerical experiments, data of electricity demand from the New York Independent System Operator (ISO) are used to verify the effectiveness of the prediction for the proposed method. The simulation results show that the proposed model can predict the next day's peak load with a considerably high accuracy compared with the ISO forecasts. © 2006 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Short Term Load Forecast Using Wavelet Neural Network

This paper presents a wavelet neural network (WNN) model combining wavelet transform and artificial neural networks for short term load forecast (STLF). Both historical load and temperature data having important impacts on load level were used in the proposed forecasting model. The model used the three-layer feed forward network trained by the error back-propagation algorithm. To enhance the forecasting accuracy by neural networks, wavelet multi-resolution analysis method was introduced to pre-process these data and reconstruct the predicted output. The proposed model has been evaluated with actual data of electricity load and temperature of Hunan Province. The simulation results show that the model is capable of providine a reasonable forecasting accuracy in STLF.     

基于FFT，DC-HC及LSTM的短期负荷预测方法

针对电力负荷序列非线性、随机性等特点引起的电力负荷预测精度低问题,提出一种基于快速傅里叶变换（FFT）、密度层次聚类算法（DC-HC）与长短时记忆（LSTM）神经网络相结合的短期负荷预测方法。首先,采用FFT计算出所有原始电力负荷序列对应的期望频率,并以之作为负荷聚类的特征量。然后采用DC-HC算法对负荷进行聚类,将原始数据分拆成具有特征属性的数据分量组;运用LSTM模型对各分量组进行负荷预测,再将各分量组预测结果进行叠加,得到最终负荷预测值;最后,采用爱尔兰实际电力负荷数据进行算例分析,结果表明所提方法能够有效提高负荷预测精度。     

一种实用的短期负荷组合预测方法

组合预测理论的发展正逐渐趋向成熟,其在电力系统负荷预测的研究与实践也逐渐增多。文中分别应用简单平均组合与最优加权组合原理,建立了短期负荷预测的组合模型。模型通过对某省负荷样本数据的分析,采用多角度选取样本,充分利用有限历史数据;同时引入多种数学模型,吸收了各模型所具有的优点。该模型具有预测精度高,建模速度快的特点。某省实际负荷数据的测试证明了模型的实用性。     

基于相似日的神经网络短期负荷预测方法

人工神经网络是模拟人脑神经元结构、特性和大脑认知功能而构成的新型信号、信号处理系统。本文针对电力负荷短期预测问题,提出了一种基于相似日的神经网络预测方法,采用反向传播算法,考虑气象因素对负荷的影响,提高了学习效能,具有较好的预测精度。本方法很适合在短期负荷预测中使用,预测结果验证了上述结论。     

一种基于进化算法的小波神经元短期负荷预测方法

提出了一种新的小波神经元网络(WNN)短期负荷预测方法。小波神经元网络比多层前馈神经网络具有更多自由度和更好的适应性。采用Morlet小波作为激活函数,应用进化算法学习网络的输入和输出之间的非线性关系。为解决小的训练误差并不表现为小的预测误差的问题,提出了一种自学习隶属度分析聚类的训练样本的选择方法。应用2002年某省电网的负荷数据和气象资料建模预测,结果表明本预测模型具有较高的预测精度和运行稳定性,普适性较好。     

基于蚁群聚类-Elman神经网络模型的短期电力负荷预测

在神经网络负荷预测实际应用中,突出的问题是训练样本大、训练时间长、收敛速度慢。针对负荷预测样本代表性问题,建立了基于蚁群聚类的Elman神经网络预测模型。对负荷历史数据进行蚁群聚类预处理,将聚类后的数据作为神经网络的训练样本。其目的是使输入样本具有代表性,改善网络训练时间和收敛速度,有效提高预测精度。通过某发电厂负荷数据的验证,该模型的预测结果精度较好。     

Improved pattern sequence‐based forecasting method for electricity load

Recently, a pattern sequence‐based forecasting (PSF) algorithm was proposed for day‐ahead electricity time series. PSF consists of two steps: clustering and prediction. However, it has the following limitations: In the clustering step, it is computationally expensive to determine the optimal number of clusters with majority votes. In the prediction step, it is quite complex to search for the matched pattern sequence with the optimal window length, and averaging all the samples immediately after the matched sequence can increase the forecasting accuracy especially when the day under examination is a working day. In this paper, we propose a time‐series forecasting method for electricity load by addressing the limitations in PSF. The proposed method is evaluated on electricity load datasets, and the experimental results show that the proposed method can improve the forecasting accuracy of PSF. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于小波分解的电力系统短期负荷预测方法研究

为提高预测精度,提出一种基于负荷分解的电力系统短期负荷预测方法。即将负荷分成周期性不同的几部分,对分解后的各负荷序列通过相匹配的神经网络方法进行预测,并考虑温度因素的影响,采用线性回归模型对神经网络预测结果修正得到最终预测结果,预测结果与实际数据对比得出,预测方法更具准确性。     

应用人工神经网与遗传算法进行短期负荷预测

针对ＢＰ网络的缺陷，提出了基于拟牛顿法的自适应算法和改进的遗传算法，以提高神经网的学习效率，克服ＢＰ网络的局部收敛性的缺点，形成一种新的神经网与遗传算法相结合的短期负荷预测模型，实测结果表明该模型和算法具有良好的性能和较高的预测精度。     

电力系统短期负荷预测的高木-关野模型研究

电力系统短期负荷预测在电力系统的运行设计中有重要的意义，利用模糊神经网络的方法进行电力负荷预测是国际上近年来很热门的一个方向。本文在传统的BP神经网络基础上，提出了一种短期负荷预测的模糊神经网络模型一高木一关野模型，以某供电局2000年的负荷实测值建立模型，进行了负荷预测，与实际值进行比较分析表明，这一模型应用于短期负荷预测能获得较高的预测精度，具有一定的研究价值。     

基于支持向量机混合模型的短期负荷预测方法

将支持向量机专家系统混合模型应用于短期负荷预测采用方法分为2个阶段:应用神经网络中的聚类算法将历史数据分割成不相连的数据域;对每个数据域选择最佳核函数预测单个SVMs。实际数据验证表明,该方法与单个多项式核、高斯核和3次样条核的SVMs预测相比具有预测精度高、支持向量少和计算量小等优点。