Based on the rising of chaos in generation load time series of Central China Power Grid, a local prediction model is established based...
最后以华中电网的发电负荷为例检验了本文中的算法，最终的预测模型的预测效果良好。
Taking the generation load time series of Central China Power Grid for example, the presented algorithm is checked and the predicted result is perfect.
最后以华中电网的发电负荷为例检验了本文中的算法，最终的预测模型的预测效果良好。
This paper discusses chaotic characteristic of the power daily load of Sichuan Province based on saturation correlation dimension, and concludes that daily load time series belong to chaotic series.
基于饱和关联维数法，对四川省全省电力系统日负荷序列的混沌特征进行定量分析，得出日负荷时间序列具有混沌性的结论。
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。
As opposed to verifying a time threshold on a method (or series of methods) in a test scenario, JUnitPerf also facilitates load testing.
与在测试场景中验证一个方法(或系列方法)的时间限制正好相反，JUnitPerf也方便了负载测试。
Two kinds of models are derived; load prediction model based on building model recognition and load prediction model based on time series analysis.
提出了两种类型负荷预报模型，基于建筑模型辩识的负荷预报法和基于时间序列的负荷预报法。
City gas load is a multi-mode and complicated engineering system. This paper analyzes and estimates the gas load of Shenzhen by using method of time series analysis.
城市燃气负荷是一个多工况、复杂的工程系统，本文采用时间序列方法对深圳的燃气负荷进行了分析和预测。
In this paper, the Theory of Corrclation Analysis on Time Series is applied to very-short-term power system load forecast.
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。
The time series analysis is proposed for load forecasting of power-generating and power transmission programming in power systems.
本文提出用于电力系统发电规划和输电规划负荷预测的时间序列分析法。
The calculation of real examples shows that the time series of these indexes is useful for the analysis of main engine real load.
通过实例计算表明，这几项指标的时间序列对分析主机及船机桨系统的实际负荷是十分有效的。
First, it introduces time series analysis principle. Then, heating load and model error prediction are given by this principle.
文中首先介绍了时间序列法预报原理，接着应用该原理给出供热负荷和模型误差的预报。
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。
Recently a new method for performing design cooling load calculations, the Radiant Time Series method (RTSM), has been developed.
近年来又出现了一种新的空调设计负荷简化计算方法——辐射时间序列方法。
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。
An improved method for short term electric load forecasting is presented. It is based on time series methods and fuzzy logic techniques.
提出一种时间序列算法和模糊逻辑技术相结合的电力系统短期负荷预测方法。
According to the characteristics of heat supply and the demands of energy-saving control, heat load forecasting based on RBF neural network and time series crossover is proposed.
针对供热过程的特点及节能控制的需要，提出基于RBF神经网络的时间序列交叉供热负荷预报法。
Electricity load can be seen as a series of random sequences, their characteristics can be analyzed by time series.
电力负荷可看作是一系列随机序列，其特性可利用时间序列方法来分析。
This paper discusses the prediction of power load by time series modelling.
本文讨论如何应用时间序列建模预测电力负荷。
Chaotic local forecasting of multivariate time series based on electric propulsion ship power load was proposed for improving the forecasting accuracy of electric propulsion ship power load.
为提高电力推进船舶电力负荷预测精度，提出电力推进船舶电力负荷的多变量混沌局部预测。
Principle of radiant time series method was precisely described and steps through which design air cooling design load calculations can be introduced.
简要介绍了辐射系数法的计算原理以及利用它计算空调设计负荷的步骤。
According to time series analysis principle, heating load with energy saving is predicted, which is taken as the set points for predictive control.
根据时间序列分析原理，对供热负荷进行节能预报并作为预测控制的设定值。
The result of predicting experiment proved that the improved power load forecasting based on time series was more efficient and more accurate.
实验证明该方法提高了电力负荷时序预测的速度和准确度。
The result of predicting experiment proved that the improved power load forecasting based on time series was more efficient and more accurate.
实验证明该方法提高了电力负荷时序预测的速度和准确度。
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