Open AccessCredibility forecasting in short‐term load forecasting and its application
Author(s) -
Li Canbing,
Li Yijing,
Cao Yijia,
Ma Jin,
Kuang Yonghong,
Zhang Zhikun,
Li Lijuan,
Wei Jing
Publication year - 2015
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2014.0729
Subject(s) - credibility , term (time) , computer science , artificial neural network , electric power system , index (typography) , probabilistic forecasting , econometrics , reliability engineering , power (physics) , artificial intelligence , engineering , economics , physics , quantum mechanics , political science , law , world wide web , probabilistic logic
It is very helpful for power system operation to assess and forecast the uncertainty of the load forecasting. The improved credibility assessment index of the short‐term load forecasting results is presented to assess the uncertainty in this study. The forecasting method for credibility of short‐term load forecasting is also proposed by adopting genetic algorithm‐based back propagation neural network. In the case study, it is proved that the improved credibility assessment can evaluate the short‐term load forecasting results and the forecasting methods. Meanwhile, the credibility forecasting can contribute to optimise reserve capacity in power system scheduling.