摘要
针对人工神经网络短期负荷预测方法的不足 ,考虑天气中的日平均气温以及特殊事件等影响负荷变化的主要因素 ,利用专家经验 ,模仿专家处理问题的方法 ,设计了一个模糊专家系统 ,对负荷预测结果进行修正 ,以提高负荷预测精度。通过合理选择模糊推理规则的形式 ,有效地减少了规则的数目 ,使人工总结专家经验并确定模糊推理规则成为可能 ,并减少了计算量 。
In view of the weakness of short term load forcasting method by artificial neural net, by regarding main factors which affect load variation such as daily average temperature, special case etc., making use of expert experience, and copying expert ways of settling problems, a fuzzy expert system is designed to correct load prediction in order to improve precision. It is possible to summarize expert experience artificially and to fix fuzzy rational regulation by selecting form and reducing numbers of regulations.
出处
《华东电力》
2000年第5期24-26,共3页
East China Electric Power
关键词
模糊专家系统
短期负荷预测
电力系统
fuzzy expert system
short term load forcasting
regulation
