Short-term load forecasting is of great importance for electric power systems.
短期电力负荷的预测对电力系统具有重要的意义。
So the study of short-term load forecasting has been paid enough attentions in the past decades.
因此，短期负荷预测方法的研究一直为人们所重视。
In this paper we propose a method for short-term load forecasting using artificial neural network.
本文提出了一种应用人工神经网络进行电力系统短期负荷预测的方法。
Gas load forecasting include: long-term, middle-term, short-term, very short-term load forecasting.
燃气负荷预测包括长期负荷预测、中期负荷预测、短期负荷预测及超短期负荷预测。
The paper presents an immune clustering RBF neural network (ICRBFNN) model for short-term load forecasting.
提出了一种免疫聚类径向基函数神经网络(ICRBFNN)模型来预测电力系统短期负荷。
The application of Subtractive Clustering Fuzzy Inference System model to forecast short-term load is presented.
采用减法聚类辅助模糊推理系统进行电力系统短期负荷预测。
Therefore, how to improve the forecasting precision is the emphasis on the study of short-term load forecasting.
因此，关于如何提高预测精度的问题，一直是短期负荷预测研究的重点问题。
A new gray theory self-correcting model is applied to the error correct of Short-term Load Forecasting Technique.
本文把一种新的灰色理论自修正模型应用到负荷预测的误差校正中。
There are traditional model methods of forecasting short-term load, such as time series, regression analysis, and so on.
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。
Looking into the present condition of power system short-term load forecast and summarizing research method in the world.
了解电力系统短期负荷预报的现状，总结国内外的研究方法。
Meteorological conditions, especially the temperature, have been the mian factors that impact the short-term load demand.
气象条件尤其是温度已成为影响电力短期负荷的主要因素。
A hybrid method based on chaos and neural network was used in the study of the electric power system short-term load forecasting.
提出了一种将混沌和神经网络相结合的方法用于短期负荷预测。
The architecture and implementation of an auto-operating short-term load forecasting system for Shenzhen power network is resumed.
概述了深圳电网自动运行的短期负荷预测系统的结构及其实现方案。
The short-term load forecasting (STLF) of electric system is one of the important routines for power dispatch and utility departments.
电力系统短期负荷预测是电力系统调度运营和用电服务部门的重要日常工作之一。
Practical operation results show that the cluster analysis method can considerably improve the accuracy of short-term load forecasting.
实际运行结果表明：利用聚类分析法进行负荷短期预测，短期负荷预测的精度大大提高。
The results of practical calculation examples show that the accuracy of forecasted short-term load can be improved by the proposed method.
最后通过模糊推理策略预测日最大负荷和日最小负荷。实际算例表明，所提出的方法能够提高短期负荷预测的精度。
The ultra short-term load algorithm is improved and made to be suitable for the real-time economic dispatcher of the distribution network.
对超短期负荷算法加以改进，使之更适合用于配电网实时经济调度。
Based on local linear prediction model of chaotic time series, short-term load forecasting method on multi-embedding dimension is presented.
基于混沌时间序列的局域线性预测模型，提出了多嵌入维的短期负荷预测方法。
A short-term load forecasting model based on SVM is presented in which the parameters in SVM are optimized by Particle Swarm Optimizer (PSO).
文章提出了PSO优化参数的SVM回归预测模型，并将其用于短期电力负荷预测。
Results show that short-term load forecasting by the developed method is sample, fast and accurate, which can be implemented on a personal computer.
计算结果表明，提出的短期负荷预测方法是简单、快速并精确的，可在个人计算机上实现。
Short-term load forecasting is the foundation of Auto Generation Control (AGC). And it is needed in running and economic distributing of power system.
超短期负荷预测将成为AGC（自动发电控制）实用化的基础，同时也是动态经济调度和电力市场运行所需的基本信息。
The corresponding BP algorithm, which brings its ability of processing non-linear problem into full play, is used to forecast the short-term load in future 24 hours.
利用相应的BP算法对未来24小时负荷进行短期预测，该方法充分发挥了神经网络处理非线性问题的能力。
In addition, the fuzzy functions for specified factors are built, and in ultra short-term load forecasting the similar load days are extracted by clustering analysis.
此外建立了一些特定因素的模糊函数，在超短期负荷预测过程中采用了聚类分析法提取负荷相似日。
Because of super short-term load forecast error, ATC is changing with the adjustment of the generation scheduling by the object function that purchasing charge is least.
考虑到超短期负荷预测偏差的情况下，以购电费用变化量最小为目标函数重新调整发电计划，从而导致电网可用输电能力(atc)发生变化。
Power system short-term load forecast is the basis of power system optimization running. It can affect safety property, reliability and economy of power system operation.
电力系统短期负荷预报是实现电力系统优化运行的基础，对于电力系统运行的安全性、可靠性和经济性都有显著影响。
This paper proposes a new short-term load forecasting method based on decision-tree approaches, which could efficiently take the non-load factors' influences into account.
提出了一种基于决策树技术的短期电力负荷预测新方法，能有效地考虑非负荷因素对短期负荷预测的影响。
In application of neural networks based short-term load forecasting model, the main problems are over many training samples, thus resulting long training time and slow convergence speed.
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。
The volatility of load time series is analyzed, and the short-term load forecasting based on SV(Stochastic Volatility) models is presented with the consideration of the time-varying characteristics.
研究了负荷时间序列波动性，考虑方差时变特征，提出了基于随机波动（SV）模型的短期负荷预测方法。
The volatility of load time series is analyzed, and the short-term load forecasting based on SV(Stochastic Volatility) models is presented with the consideration of the time-varying characteristics.
研究了负荷时间序列波动性，考虑方差时变特征，提出了基于随机波动（SV）模型的短期负荷预测方法。
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