A New Strategy for Short-Term Load Forecasting
A New Strategy for Short-Term Load Forecasting
复制标题
短期负荷预测的新策略
DOI：
10.1155/2013/208964
复制
发表时间：
2013-05
影响因子：
--
通讯作者：
Caihong Li
中科院分区：
文献类型：
--
作者：
Yi Yang;Jie Wu;Yanhua Chen;Caihong Li
电力是一种难以储存的特殊能源，因此电力需求预测一直是一个重要的问题。准确的短期负荷预测在电力系统中起着至关重要的作用，因为它是电力系统规划和运行的重要组成部分，也是许多应用中的基础。针对单一预测模型对短期负荷预测效果不佳的问题，提出了一种基于季节性ARIMA模型和BP神经网络的混合预测模型，以提高短期负荷预测精度。首先采用季节性ARIMA模型对前一天的电力负荷需求进行预测;然后将预测过程中得到的剩余负荷需求序列作为原始序列，利用BP神经网络对后续的剩余序列进行预测;最后将预测的剩余序列与季节性ARIMA模型得到的预测负荷需求序列相加，得到最终的负荷需求预测序列。实例分析表明，新策略对提高短时负荷预测的准确性是十分有效的。
Electricity is a special energy which is hard to store, so the electricity demand forecasting remains an important problem. Accurate short-term load forecasting (STLF) plays a vital role in power systems because it is the essential part of power system planning and operation, and it is also fundamental in many applications. Considering that an individual forecasting model usually cannot work very well for STLF, a hybrid model based on the seasonal ARIMA model and BP neural network is presented in this paper to improve the forecasting accuracy. Firstly the seasonal ARIMA model is adopted to forecast the electric load demand day ahead; then, by using the residual load demand series obtained in this forecasting process as the original series, the follow-up residual series is forecasted by BP neural network; finally, by summing up the forecasted residual series and the forecasted load demand series got by seasonal ARIMA model, the final load demand forecasting series is obtained. Case studies show that the new strategy is quite useful to improve the accuracy of STLF.
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影响因子：
6.6
作者：
T. Senjyu;P. Mandal;K. Uezato;T. Funabashi
通讯作者：
T. Senjyu;P. Mandal;K. Uezato;T. Funabashi
DOI：
10.1175/1520-0450(2001)040
发表时间：
2001-08
期刊：
Journal of Applied Meteorology
影响因子：
--
作者：
E. Valor;V. Meneu;V. Caselles
通讯作者：
E. Valor;V. Meneu;V. Caselles
影响因子：
64.8
作者：
RUMELHART, DE;HINTON, GE;WILLIAMS, RJ
通讯作者：
WILLIAMS, RJ
影响因子：
8.5
作者：
Chen, Kuan-Yu;Wang, Cheng-Hua
通讯作者：
Wang, Cheng-Hua
影响因子：
3.9
作者：
Wang, Bo;Tai, Neng-ling;Qi, Liang-bo
通讯作者：
Qi, Liang-bo