针对目前电力系统负荷建模的现状，指出电力负荷建模研究的发展趋势。
According to the present status and the needs from the developments of power systems, the research tendencies of electric load modeling are pointed out in this paper.
算例中利用实测数据进行负荷动态建模，结果表明可得到精度和泛化能力都较高的负荷模型，在电力负荷建模方面具有广泛的应用价值。
In the calculation example, the real data were used for the dynamic modeling of the load, and the results show that the load model is accurate and has high generalization ability.
论述了电力负荷预测中建模数据的选择、预处理方法及其对预测精度的影响。
The selection of the modeling data, the data's pretreatment of power load forecasting and their effects on forecasting precision were discussed.
针对电力负荷预测的实际困难，提出了一种以节气负荷作为建模数据，将ARIMA模型及BP网络相结合的负荷预测新方法。
Aiming at the actual difficulties in load forecasting, a new load forecasting method in which the solar term load is used as modeling data was put forward combining ARIMA model and BP network.
针对传统灰色模型的建模机理和存在的局限性，提出了改进方法，建立了新的电力负荷预测模型。
Aiming at the modeling mechanism and limitation of traditional grey model, puts forward the improvement method, and sets up the new electric power load forecast model.
本文讨论如何应用时间序列建模预测电力负荷。
This paper discusses the prediction of power load by time series modelling.
开发了一个电力系统综合负荷建模软件平台。
A power aggregate load modeling software platform was developed.
电力系统短期负荷预测使用的方法有传统建模方法，诸如时间序列、回归分析等方法。
There are traditional model methods of forecasting short-term load, such as time series, regression analysis, and so on.
对于大扰动事件，提出了利用电力故障录波系统信息(PFRMS)和SVM的负荷建模。
As for large disturbances, this dissertation used SVM and information of power fault recording and monitoring system (PFRMS) to establish load model.
同时，作为电力系统数学建模的重要元件，负荷建模的准确性对研究结果的正确性产生重要的影响。
At the same time, as the power system is the important component of mathematical modeling, load modeling accuracy for the correctness of the research results have important influence.
电力系统负荷预报模型因负荷构成及采用的建模技术不同而各异。
Load forecasting models in power system is different due to load constitutes and modeling technique.
本文主要讨论在电力负荷预测模型，它是基于支持向量机的建模方法。
The modeling method of electrical load forecasting model, which is based on SVM, is mainly discussed in this paper.
着重论述了电力负荷预测中建模变量的选择、数据的预处理方法、模型的拓扑结构及其对预测精度的影响。
The variable selection, data preprocessing and model structure of power load forecasting, and their effects on forecasting precision are discussed.
着重论述了电力负荷预测中建模变量的选择、数据的预处理方法、模型的拓扑结构及其对预测精度的影响。
The variable selection, data preprocessing and model structure of power load forecasting, and their effects on forecasting precision are discussed.
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