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负荷序列
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  power load series
     In this paper,the characteristic clustering and its analysis to power load series based on Ant Colony Optimization Algorithm(ACOA) was presented.
     该文提出了基于蚁群优化算法(ant colony optimization Algorithm,ACOA)的电力系统负荷序列聚类分析。 通过对实际地区负荷系统的聚类分析显示其优越性;
短句来源
     In this paper, the clustering analysis of power load series basedon ACOA is first time presented. Compared with Kohonen neural network used inpower load clustering usually, the load clustering performance of ACOA in actualload system has shown its superiority, which has more sensitivity and resolution toclimatic factors and high temperature area;
     本论文首次把ACOA用于电力负荷序列聚类分析,并与广泛用于电力负荷聚类的Kohonen神经网络进行对比,经实际地区负荷系统仿真测试证明了基于ACOA的聚类性能优越性,它对气候异常情况、高温区域、节假日都具有更高的敏感性和分辨率;
短句来源
     To use power load series for characteristic clustering combination with pattern recognition may use as one method of solving the problem.
     运用负荷序列特征的聚类分析与模式识别相结合原理可解决该问题。
短句来源
     Firstly,the power load series is adaptively decomposed into a series of stationary intrinsic mode functions(IMF) in different scale space. The local features of original load series are prominent in the IMF so that it is more obvious to observe the cycle,random and trend parts of the original load sequence.
     首先,运用EMD将负荷序列自适应地分解成一系列不同尺度的本征模式分量IMF(intrinsic mode function),分解后的分量突出了原负荷的局部特征,能更明显地看出原负荷序列的周期项、随机项和趋势项;
短句来源
  load series
     In this paper,the characteristic clustering and its analysis to power load series based on Ant Colony Optimization Algorithm(ACOA) was presented.
     该文提出了基于蚁群优化算法(ant colony optimization Algorithm,ACOA)的电力系统负荷序列聚类分析。 通过对实际地区负荷系统的聚类分析显示其优越性;
短句来源
     RESEARCH OF FORECASTING METHOD ON CHAOTIC LOAD SERIES WITH HIGH EMBEDDED DIMENSION
     高嵌入维混沌负荷序列预测方法研究
短句来源
     In the study of load series analysis, a new dimension reduction method for load series based on feature points is proposed, then a hierarchical clustering based on density (HCD) is applied to find similar load pattern and special load pattern.
     在负荷序列分析研究中,首先针对负荷序列的高维性,使用基于特征点的时间序列分段方法进行降维,然后采用基于密度的层次聚类算法(HCD)进行聚类分析。
短句来源
     Firstly,the power load series is adaptively decomposed into a series of stationary intrinsic mode functions(IMF) in different scale space. The local features of original load series are prominent in the IMF so that it is more obvious to observe the cycle,random and trend parts of the original load sequence.
     首先,运用EMD将负荷序列自适应地分解成一系列不同尺度的本征模式分量IMF(intrinsic mode function),分解后的分量突出了原负荷的局部特征,能更明显地看出原负荷序列的周期项、随机项和趋势项;
短句来源
     The clustering analysis approach of electric load series based on improved datamining arithmetic is creatively proposed in the paper, and it proposed the notion ofthe variance of difference sequence.
     本文创造性地提出了基于数据挖掘改进算法的电力系统负荷序列聚类分析方法,首次提出负荷序列间差分序列方差的概念。
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  “负荷序列”译为未确定词的双语例句
     In order to find out the most effective variables which will be inputs of artificial neural network, this paper presents a new method, which involves correlation coefficient method to quantities the relation between many factors and bus load.
     提出了应用关联度分析的方法量化各变量之间的关联程度,通过综合分析历史负荷与其相关因素的关联度以及负荷序列自身的自相关系数选取神经网络的输入变量,更合理地提取了对母线负荷预测最为有效的变量,从而确定简单有效的网络结构。
短句来源
     In addition, a bad data elimination method based on the wavelet analysis is adopted. By using the wavelet transform, the different load sequence components are projected to the different scales in which the matching modulus maxima can be obtained and eliminated according to the daily-period feature of the power system load.
     此外,为了删除历史负荷数据中夹杂的异常数据以便提高预测精度,采用了一种伪数据处理方法:利用小波变换,将负荷序列投影到不同的尺度上,在不同的尺度域分别计算模极大值,并根据负荷以天为周期波动的特性对模极大值进行处理,最后通过小波重构得到去处伪数据的负荷序列
短句来源
     From the viewpoint of nonlinear dynamic system theory, the substance of load forecasting by the loads in similar days is the forecasting for the behaviors of equilibrium points and quasi-periods.
     从非线性动力系统理论角度看,相似日预测的实质是对负荷序列中平衡点和准周期行为的预测。
短句来源
     Chaotic characteristic quantity of hourly power load of Sichuan province is analyzed, that is, saturation correlation dimension, maximum Lyapunov exponent and Kolmogorov entropy, and it is concluded that hourly load time series belongs to chaotic series.
     通过计算四川全省电网小时负荷时间序列的混沌特征量:饱和关联维数、最大Lyapunov指数和Kolmogorov熵,论证了该小时负荷序列属于混沌时间序列。
短句来源
     Firstly,based on wavelet multi-resolution analysis method,the load serials are decomposed to different sub-serials which show the different frequency characteristics of the load.
     首先基于小波多分辨率分析方法将负荷序列分解成具有不同频率特征的序列。
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  load series
During both static load series a certain amount of micromotion between stem and implantation bed occurred in the frontal and sagittal planes and also during rotation.
      
The forecasting performance, over ten different load series, shows satisfactory results when the sparse representation is built with less than 3% of the available sample.
      
For each of the selected hours, four high-resolution load series were selected from the Xcel historical data.
      
Similar to the basal water load series, blood pressure increased with L-NAME and actually increased further as the volume was infused.
      
This net load series and an estimated series of water available to the BPA hydro system serve as inputs to the BPA hydro simulation tools.
      
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The self-tuning technology is a new branch of the mordern control theory. In this paper we try to present the application of the self tuning prediction approach to the power system load forecasting. After a short introduction of its basic principle, a new power system load model is proposed in the paper, as it is the key-point of the forecasting problem. Simulation with a set of real power system ioad data shows that the average prediction error is about 3.24%. It also demonstrates that the approach has a number...

The self-tuning technology is a new branch of the mordern control theory. In this paper we try to present the application of the self tuning prediction approach to the power system load forecasting. After a short introduction of its basic principle, a new power system load model is proposed in the paper, as it is the key-point of the forecasting problem. Simulation with a set of real power system ioad data shows that the average prediction error is about 3.24%. It also demonstrates that the approach has a number of advantages such as simple algorithm, good convergence, etc.. It is considered that the approach is applicable while further work is needed for its practical application.

自校正技术是现代控制理论中的一个新分支。本文的目的是要将自校正预测技术应用于电力系统负荷预报。为此,首先简要地叙述了自校正预测器的基本原理,然后根据电力负荷序列的特性提出一个新的负荷模型。少量试验结果给出预报误差均值为3.24%,整个过程体现了自校正预测器算法简单,收敛性好等优点,该方法是可行的。

This paper presents an intelligent system for forecasting the short term load being used. Directed at the non-stationarity of load, this system can eliminate the influence of abnormal data on forcasting, check and amend the forecasting result, automatically modify the model parameters based on the forecasting errors, and give out better fore-casting result by grouping different models. This system has its own character for the fore-casting during the holidays. The results of load forecasting show that this system...

This paper presents an intelligent system for forecasting the short term load being used. Directed at the non-stationarity of load, this system can eliminate the influence of abnormal data on forcasting, check and amend the forecasting result, automatically modify the model parameters based on the forecasting errors, and give out better fore-casting result by grouping different models. This system has its own character for the fore-casting during the holidays. The results of load forecasting show that this system has intelligence in a certain degree and good adaptability for the nonstationary load series.

本文介绍一个正在实际使用的短期电力负荷智能预报系统。针对电力负荷变化的非平稳性,该系统能消除异常数据对预报值的影响,能对预报结果进行检验及修正,能根据预报误差自动校正预报模型参数,能根据“近大远小”的原则进行预报,并能将多种不同的数学模型加权组合预报。该系统对节日负荷预报也有自己的特色。实际使用结果表明,该系统具有一定的智能性,对非平稳的电力负荷序列有很好的自适应性。

In this paper, the Theory of Corrclation Analysis on Time Series is applied to very-short-term power system load forecast. System load series is coincided mith Multiply Seasonal ARIMAmodel. The Fast Online Coincidence Algorithm of this model is given in this paper. Load forecast is implemanted with Inverse Function Method and Smoothing Method. Practical calculations hawe shown that the algorithms in this paper can satisfy the demands of online calculation which requires rapidity and precision.

本文将时间序列相关分析理论应用于电力系统超短期负荷预报。用乘积型周期性ARIMA模型在线拟合系统负荷序列,并给出了模型的快速在线拟合算法。采用逆函数法并辅以平滑处理完成负荷预报。实例计算表明,本文算法预报速度快,预报精度高。

 
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