英语翻译因为是新手,所以没有财富可悬赏,请大家多多见谅In previous work,we applied artificial neural networks (ANN) for short term load forecasting using real load and weather data from the Hydro-Quebec databases where three typ

英语翻译因为是新手,所以没有财富可悬赏,请大家多多见谅In previous work,we applied artificial neural networks (ANN) for short term load forecasting using real load and weather data from the Hydro-Quebec databases where three typ
英语翻译
因为是新手,所以没有财富可悬赏,请大家多多见谅
In previous work,we applied artificial neural networks (ANN) for short term load forecasting using real load and weather data from the Hydro-Quebec databases where three types of variables were used as inputs to the neural network:(a) hour and day indicators,(b) weather related inputs and (c) historical loads.In general,for forecasting with a lead time of up to a few days ahead,load history (for the last few days) is not available,and therefore,estimated values of this load are used instead.However,a small error in these estimated values may grow up dramatically and lead to a serious problem in load forecasting since this error is fed back as an input to the forecasting procedure.In this paper,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.
_ 2006 Elsevier Ltd.All rights reserved.
Keywords:Power systems; Load forecasting; Artificial neural networks

英语翻译因为是新手,所以没有财富可悬赏,请大家多多见谅In previous work,we applied artificial neural networks (ANN) for short term load forecasting using real load and weather data from the Hydro-Quebec databases where three typ
在以往的工作中,我们运用人工神经网络(ANN)为短期负荷预测实际负载和天气的地方Hydro-Quebec数据库数据的三种类型的变量作为输入与神经网络:(一)时指标(b)天气相关的输入(c)历史负荷.一般来说,对于预测与领先的时间在几天前,加载历史(最近几天),因此,估计价值的负荷来代替.然而,一个小错误的估计价值在长大可能急剧减少,导致了严重的问题在负荷预测,因为这误差反馈预测作为输入程序.在本文中,我们展示安能力负荷预测的加载历史作为输入.此外,只有温度(从天气变量),在这种应用,结果表明,其他变量如天候(云层覆盖)和风速没有严重的影响及可能不会被认为是在负荷预测的程序.
2006年公司__卷.版权所有.
关键词:电力系统负荷预测;;人工神经网络