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  • 标题:Synthesis Load Forecasting Method Based on Artificial Immune System for Power System
  • 本地全文:下载
  • 作者:Nan Dong ; Haojun Zhu ; Yunhua Xi
  • 期刊名称:E3S Web of Conferences
  • 印刷版ISSN:2267-1242
  • 电子版ISSN:2267-1242
  • 出版年度:2019
  • 卷号:118
  • 页码:1-4
  • DOI:10.1051/e3sconf/201911802051
  • 语种:English
  • 出版社:EDP Sciences
  • 摘要:Electric power load forecasting is not only the sticking point of the safely, operation of whole system, but also the key part of the economical and healthy development of electric power system. The intrinsic single models have shortage, so the synthesis forecasting model making better use of all information will be pursued. It combines those single models property to take full advantage of their information to improve the precision. The most important part of the combination forecasting model is how to confirm the weight. In AIS, antigen and antibody are the parallelism of aim function and doable result. The appetency between antigen and antibody is regarded as the matching degree between feasible result and the objective function. Because of its good property on global searching, it can find the optimal solutions, some synthetic forecasting models based on AIS are set up in this paper, which combine AIS and load forecasting. The attempter average synthetic model and power geometry average synthetical model proposed in this paper, has been applied to a certain area mid-long term load forecasting. It is showed that the synthetic forecasting model based on AIS could provide high forecasting precision.
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