A data mining method for short‐term load forecasting in power systems

This paper proposes a method for daily maximum load forecasting in power systems. It is based on the integration of the regression tree and the artificial neural network. In this paper, the regression tree is used to extract knowledge or rules as a data‐mining method. That is useful for the information processing of the complicated data. As a result, the proposed method has an advantage in clarifying the cause and effect of dynamic load behavior in load forecasting. However, the regression tree does not necessarily yield good prediction results in spite of good classification. Therefore, this paper proposes a method for combining the classification results of the regression tree with the multilayer perceptron of a universal nonlinear approximator. The effectiveness of the proposed method is demonstrated in real data. © 2002 Scripta Technica, Electr Eng Jpn, 139(2): 12–22, 2002; DOI 10.1002/eej.1150     

Reconstructing clusters for preconditioned short‐term load forecasting

This paper presents a new preconditioned method for short‐term load forecasting that focuses on more accurate predicted value. In recent years, the deregulated and competitive power market increases the degree of uncertainty. As a result, more sophisticated short‐term load forecasting techniques are required to deal with more complicated load behavior. To alleviate the complexity of load behavior, this paper presents a new preconditioned model. In this paper, clustering results are reconstructed to equalize the number of learning data after clustering with the Kohonen‐based neural network. That enhances a short‐term load forecasting model at each reconstructed cluster. The proposed method is successfully applied to real data of one‐step ahead daily maximum load forecasting. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 161(1): 26–33, 2007; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20300     

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 forecasting with increment regression tree

This paper presents a new regression tree method for short-term load forecasting. Both increment and non-increment tree are built according to the historical data to provide the data space partition and input variable selection. Support vector machine is employed to the samples of regression tree nodes for further fine regression. Results of different tree nodes are integrated through weighted average method to obtain the comprehensive forecasting result. The effectiveness of the proposed method is demonstrated through its application to an actual system.     

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     

数据挖掘与非正常日的负荷预测

提高非正常日的负荷预测精度是当前负荷预测工作的难点。文中提出了一种基于知识库的事先判别突变并做出适当处理的预测流程,介绍了利用数据挖掘的决策树技术建立知识库的方法,并给出了几种典型的非正常日修正模型。最后,通过对长时期负荷预测数据的统计分析,说明了新方法的有效性和实用性。     

模糊线性回归法在负荷预测中的应用

线性回归是电力系统中期负荷预测的常用方法。由于受众多不确定性因素的影响，历史数据和相关变量未来取值常常是不准确的，致使该方法的预测结果误差较大。为了提高电力负荷预测的精度，作者提出了一种改进的模糊线性回归预测方法，即加权模糊线性回归预测法，它将模糊线性回归法预测模型的求解归结为一个线性规划问题，并对该模型进行改进，按照回归变量的重要程度确定目标函数中各项的权重，并按照各历史数据的重要程度确定贴近度标准。文中提出的改进模型即加权模糊线性回归模型是可调的，能够灵活计及预测中的一些定性模糊因素。实际算例表明，文中的改进措施提高了模糊线性回归法的预测精度。     

Development of very‐short‐term load forecasting based on chaos theory

It is indispensable to accurately perform short‐term load forecasting of 10 minutes ahead in order to avoid undesirable disturbances in power system operations. The authors have so far developed such a forecasting method based on conventional chaos theory. However, this approach cannot give accurate forecasting results when the loads consecutively exceed the historical maximum or are less than the minimum. Electric furnace loads with steep fluctuations are another factor degrading the forecast accuracy. This paper presents an improved forecasting method based on chaos theory. In particular, the potential of the Local Fuzzy Reconstruction Method, a variant of the localized reconstruction methods, is fully exploited to realize accurate forecasting as much as possible. To resolve the forecast deterioration due to suddenly changing loads such as by electric furnaces, they are separated from the rest and smoothing operations are carried out afterwards. The separated loads are forecasted independently from the remaining components. Several error correction methods are incorporated to enhance the proposed forecasting method. Furthermore, a consistent measure of obtaining the optimal combination of parameters to be used in the forecasting method is presented. The effectiveness of the proposed methods is verified by using real load data for 1 year. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 148(2): 55–63, 2004; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10322     

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     

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.     

利用随机森林回归的现货市场出清价格预测方法

为得到一种实用性较强且具有较高精度的电力现货市场出清价格的预测方法,该文尝试将随机森林回归应用到现货市场出清价格预测。首先通过随机森林回归的特征重要度分析功能对历史出清价和负荷输入进行特征筛选,然后建立基于随机森林回归的市场出清价预测模型,以网格搜索和交叉验证的方法确定模型参数,最后与基于决策回归树、支持向量机回归和人工神经网络的方法在北欧现货市场公开数据的基础上进行对比试验。试验结果表明该文设计预测方法相较其他方法的平均预测精度至少提高了25%,且预测效果较为稳定,同时输入特征筛选方法的应用能够进一步提高各个模型的预测精度。     

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.     

A current sensorless MPPT control method for a stand‐alone‐type PV generation system

In this paper, a current sensorless MPPT control method for a stand‐alone‐type PV generation system is proposed. This control method offers advantages of the simplified hardware configuration and low cost by using only one sensor to measure the PV output voltage. When used as a stand‐alone‐type with a battery load, the experimental results show that the estimated values of PV output current are accurate, and the use of the proposed MPPT control increases the PV generated energy by 16.3% compared to the conventional system. Furthermore, the authors clarified that the proposed method has an extremely high UUF (useful utilization factor) of 98.7%. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 157(2): 65– 71, 2006; Published online in Wiley InterScience ( www.interscience. wiley.com ). DOI 10.1002/eej.20424     

短期负荷预测的实用数据挖掘模型

基于数据挖掘决策树算法和通用的决策支持对象(DSO)建模工具,结合区域电网气象负荷数据库设计与实现了决策树形式的数据挖掘模型并运用于日负荷预测。首先描述了决策树分类方法,分析研究了日负荷预测数据挖掘模型的决策树构造过程,给出了基于DSO的程序化实现,并给出用决策树算法的日负荷预测过程以及实际的预测结果情况。统计分析结果表明该数据挖掘模型完全满足实用标准,具有智能自适应、自学习和全过程自动化、通用可靠以及准确率高等特性。     

Acquisition of optimal attribute subset through genetic algorithm using GNP‐based class association rule mining

Attribute selection is a technique to prune less relevant information and discover high‐quality knowledge. It is especially useful for the classification of a large database, because the preprocessing of data increases the possibility that predictor attributes given to the mining algorithm become more relevant to the class attribute. In this paper, a method to acquire the optimal attribute subset for the genetic network programming (GNP) based class association rule mining has been proposed, and this attribute selection process using genetic algorithm (GA) leads to a higher accuracy for classification. Class association rule mining through GNP is conducted with a small subset of data rather than the original large number of attributes; thus simple but important rules are obtained for classification while the local optimal problem is avoided. Simulation results with educational data show that the classification accuracy is largely improved from 52.73 to 74.54%, when classification is made using the optimal attribute subset. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于CNN-SAEDN-Res的短期电力负荷预测方法

基于深度学习的序列模型难以处理混有非时序因素的负荷数据,这导致预测精度不足。提出一种基于卷积神经网络(CNN)、自注意力编码解码网络(SAEDN)和残差优化(Res)的短期电力负荷预测方法。特征提取模块由二维卷积神经网络组成,用于挖掘数据间的局部相关性,获取高维特征。初始负荷预测模块由自注意力编码解码网络和前馈神经网络构成,利用自注意力机制对高维特征进行自注意力编码,获取数据间的全局相关性,从而模型能根据数据间的耦合关系保留混有非时序因素数据中的重要信息,通过解码模块进行自注意力解码,并利用前馈神经网络回归初始负荷。引入残差机制构建负荷优化模块,生成负荷残差,优化初始负荷。算例结果表明,所提方法在预测精度和预测稳定性方面具有优势。     

An efficient class association rule‐pruning method for unified intrusion detection system using genetic algorithm

Genetic network programming (GNP)‐based class association rule mining has been demonstrated to be efficient for misuse and anomaly detection. However, misuse detection is weak in detecting brand new attacks, while anomaly detection has a defect of high positive false rate. In this paper, a unified detection method is proposed to integrate misuse detection and anomaly detection to overcome their disadvantages. In addition, GNP‐based class association rule mining method extracts an overwhelming number of rules which contain much redundant and irrelevant information. Therefore, in this paper, an efficient class association rule‐pruning method is proposed based on matching degree and genetic algorithm (GA). In the first stage, a matching degree‐based method is applied to preprune the rules in order to improve the efficiency of the GA. In the second stage, the GA is implemented to pick up the effective rules among the rules remaining in the first stage. Simulations on KDDCup99 show the high performance of the proposed method. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

A useful identification method to build low‐order transfer function models based on frequency response for small‐signal stability analysis

This paper presents a scheme of small‐signal stability analysis for very large radial power systems. Generally, a radial power system can be easily classified as one main system and some external systems. Therefore, if accurate low‐order model of the external systems could be identified, the analysis effort for small‐signal stability can be reduced. Some dynamic reduction methods are proposed. Especially, the frequency‐domain least‐squares approximation methods, which are powerful and have high numerical reliability. This paper proposes a modal rebuild logic to improve the result obtained by the frequency‐domain least‐squares approximation methods. The proposed method provides high accuracy and a practical low‐order transfer function model. This paper introduces the usefulness of the proposed method with some numerical examples. In addition, merging sophisticated data handling and advanced applications in order to reduce human efforts is also discussed. This paper mentions the importance of automated node ID handling in order to realize the classification of system data into some partial data sets. © 2006 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于数据挖掘技术和支持向量机的短期负荷预测

支持向量机方法已经非常成熟的应用在短期负荷预测领域,它在选取历史日期进行模型训练的时候通常选取距离预测日相近的一段日期,而没有考虑这段时间气象条件、星期类型、节假日造成的影响,使得所建立的模型并不能完全的反映预测日的特征。提出了基于一种基于数据挖掘技术的支持向量机负荷预测方法,该方法提出了预测模型样本选取的新颖思路,首先采用层次聚类法对历史日负荷进行聚类,利用层次聚类得到的分类结果建立决策树,根据待预测日的属性在决策树中查询得到支持向量机预测模型输入的历史负荷,建立支持向量机预测模型并对待预测日的负荷进行预测。实例中负荷数据采用浙江省某地级市的历史负荷,用新方法对该地区的日96点负荷进行预测,并将该算法与传统的支持向量机算法进行比较,文中提出的方法解决了传统的基于支持向量机方法训练日期选取不能反映待预测日特征的问题,故本算法结果具有较高预测精度。     

基于Prophet与XGBoost混合模型的短期负荷预测

准确有效的预测电力负荷对电网的安全稳定运行具有重要的参考价值.通过对Prophet框架和XGBboost(eXtreme gradient boosting)机器学习模型的深度分析,提出了基于Prophet与XGBoost的混合电力负荷预测模型,针对大量的历史电负荷数据、日期信息、气象数据,分别构建Prophet电力负...