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adjustable grey model
相关语句
  可调灰色模型
     ADJUSTABLE GREY MODEL FOR LOAD PREDICTION IN POWER SYSTEMS AND ITS APPLICATION
     电力系统负荷预测的可调灰色模型及其应用
短句来源
     Periodical Adjustable Grey Model for Prediction of Electric Loads
     电力系统负荷预测的周期性可调灰色模型
短句来源
     In view of the non-stationarity and periodically of electric load, the auther first sets up the grey model or adjustable grey model for the analysis of the load trend. By comparing grey model values with the original ones, the auther gets a series of errors,then by means of the secondary data, the auther further applies autoregressive model, fourier model or artificial neural network model to the adaptability modification of the errors resulting from the grey model.
     本文针对电力负荷变化的非平稳性和周期性,采用灰色模型,可调灰色模型分析用电负荷的趋势项并与历史负荷比较得一系列残差,然后应用自回归模型,傅氏模型,人工神经网络模型进行修正以提高精度。
短句来源
  相似匹配句对
     Adjustable Grey GM(1,1) Model
     可调式灰色GM(1,1)模型
短句来源
     Grey Engel model
     灰色恩格尔模型(GEM)
短句来源
     Grey forecasting model
     灰色预测模型
短句来源
     Periodical Adjustable Grey Model for Prediction of Electric Loads
     电力系统负荷预测的周期性可调灰色模型
短句来源
     ADJUSTABLE GREY MODEL FOR LOAD PREDICTION IN POWER SYSTEMS AND ITS APPLICATION
     电力系统负荷预测的可调灰色模型及其应用
短句来源
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The general grey model for load prediction in power systems is improved by introducing two adjustable parameters so as to turn it into an adjustable grey prediction model. The restrictions against setting up the model are decreased and the scope of application is more wide-spread. The results of prediction are controllable and adjustable, thus improving the accuracy of prediction.For comparison, four prediction methods, including the adjustable grey model are used to predict the power load of Wuxi city,...

The general grey model for load prediction in power systems is improved by introducing two adjustable parameters so as to turn it into an adjustable grey prediction model. The restrictions against setting up the model are decreased and the scope of application is more wide-spread. The results of prediction are controllable and adjustable, thus improving the accuracy of prediction.For comparison, four prediction methods, including the adjustable grey model are used to predict the power load of Wuxi city, Jiangsu Province. The adjustable grey prediction model proves to be more practical.

本文对电力系统负荷预测的普通灰色模型进行了可调性改进,成为可调灰色预测模型,减少了建模的限制条件,应用范围更加广泛,可以控制、调整预测结果,使精度提高。文中对江苏省无锡市电力负荷使用四种预测方法比较,从理论和应用两方面证实了可调灰色模型明显的实用性。

In view of the non-stationarity and periodically of electric load, the auther first sets up the grey model or adjustable grey model for the analysis of the load trend. By comparing grey model values with the original ones, the auther gets a series of errors,then by means of the secondary data, the auther further applies autoregressive model, fourier model or artificial neural network model to the adaptability modification of the errors resulting from the grey model....

In view of the non-stationarity and periodically of electric load, the auther first sets up the grey model or adjustable grey model for the analysis of the load trend. By comparing grey model values with the original ones, the auther gets a series of errors,then by means of the secondary data, the auther further applies autoregressive model, fourier model or artificial neural network model to the adaptability modification of the errors resulting from the grey model. Taking as an example show that these methods is efficient. The forecasting accuracy is more accurate than that of conventional methods.

本文针对电力负荷变化的非平稳性和周期性,采用灰色模型,可调灰色模型分析用电负荷的趋势项并与历史负荷比较得一系列残差,然后应用自回归模型,傅氏模型,人工神经网络模型进行修正以提高精度。用一系列组合模型分别用于不同场合和要求下的负荷预测,并在微机上开发软件,通过实例计算,效果良好,具有一定的应用价值

In view of in-contrallability and periodicity of electric loads, the authors analyse electric loads by using "census Ⅱ " decomposition method and get a series of season factors and terms of load trend. Then the authors apply the adjustable grey model to forecast electric loads. A real example shows that the method is efficient.

本文针对电力系统负荷变化的不可控性和周期性,通过采用Census Ⅱ分解方法分析季节因子,与历史负荷相比较,得到一系列长期趋势项,然后用可调灰色模型对分离出的长期趋势项进行预测,并用二分法调整u,v的取值,使得误差最小.将预测出的季节因子与长期趋势项重新组合,就得到日用电量的预测结果,实例表明,本系统对于呈周期性变化的电力负荷具有较好的预测效果.

 
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