紧急!!!请帮忙翻译一下下面的内容,非常感谢!!!
因为是新手,所以没有财富可悬赏,请大家多多见谅Thesystemloadforecastingmodelisacriticallyimportantdecisionsup...
因为是新手,所以没有财富可悬赏,请大家多多见谅
The system load forecasting model is a critically important decision support tool for operating the electric power system securely and economically. Because of their input-output mapping ability, artificial neural networks are well suited for this type of applications, In this study, an investigation on the use of ANNs for short term load forecasting for the Hydro-Quebec system has been conducted where we demonstrate ANN capabilities in load forecasting without the use of load history as an input. In addition, only temperature (from weather variables) is used, in this application, where results show that other variables like sky condition (cloud cover) and wind velocity have no serious effect and may not be considered in the load forecasting procedure.
A simple multi-layered feedforward ANN has been used, and results show that the ANN is able to interpolate among the load and weather variables pattern data of training sets to provide the future load pattern. However, these are preliminary results. The possibility for better results exists and can be achieved by using: (1) more advanced types of ANN, (2) better selection of input variables, (3) better ANN architecture and (4) better selection of the training set.
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The system load forecasting model is a critically important decision support tool for operating the electric power system securely and economically. Because of their input-output mapping ability, artificial neural networks are well suited for this type of applications, In this study, an investigation on the use of ANNs for short term load forecasting for the Hydro-Quebec system has been conducted where we demonstrate ANN capabilities in load forecasting without the use of load history as an input. In addition, only temperature (from weather variables) is used, in this application, where results show that other variables like sky condition (cloud cover) and wind velocity have no serious effect and may not be considered in the load forecasting procedure.
A simple multi-layered feedforward ANN has been used, and results show that the ANN is able to interpolate among the load and weather variables pattern data of training sets to provide the future load pattern. However, these are preliminary results. The possibility for better results exists and can be achieved by using: (1) more advanced types of ANN, (2) better selection of input variables, (3) better ANN architecture and (4) better selection of the training set.
References 展开
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该系统负荷预测模型是一种极为重要的决定,对电力系统运行的安全和经济上的支持工具。由于其输入输出映射能力,人工神经网络非常适合这种类型的应用,在此研究中,对短期的液压系统进行了魁北克,我们证明人工神经网络负荷预测人工神经网络利用现状调查在电力负荷预测的负荷能力没有历史作为输入使用。此外,只有温度(从天气变量)的使用,在此应用程序,如结果表明,像天空条件(云层)和风速等变量有没有严重影响,而且可能不会在负荷预测程序审议。
一个简单的多层前馈神经网络已被使用,结果表明,人工神经网络是能够插负荷和天气之间的变量数据集的训练模式,以提供未来的负载模式。然而,这些只是初步的结果。为更好的结果的可能性存在,可以通过实现:(1)人工神经网络更先进的类型,(2)更好的输入变量的选择,(3)更好的神经网络结构和(4)选择更好的训练集。
参考资料
一个简单的多层前馈神经网络已被使用,结果表明,人工神经网络是能够插负荷和天气之间的变量数据集的训练模式,以提供未来的负载模式。然而,这些只是初步的结果。为更好的结果的可能性存在,可以通过实现:(1)人工神经网络更先进的类型,(2)更好的输入变量的选择,(3)更好的神经网络结构和(4)选择更好的训练集。
参考资料
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