计算机科学 ›› 2010, Vol. 37 ›› Issue (4): 212-.

• 人工智能 • 上一篇    下一篇

基于知识发现的电力负荷预测模型

窦全胜,史忠植,于尔铿,杨斌,刘仲尧   

  1. (山东工商学院计算机科学与技术学院 烟台264005);(黑龙江省电力有限公司 哈尔滨150010);1(中国科学院计算技术研究所 北京100080);(烟台东方电子信息产业集团有限公司 烟台264001)
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金(60775035),国家重点基础研究发展计划资助项目(2007CB311004),Ir}I家高技术研究发展计划项目(2007AA01Z32)资助。

Power Load Forecasting Model Based on Knowledge Discovery

DOU Quan-sheng,SHI Zhong-zhi,YU Er-keng,YANG Bin,LIU Zhong-yao   

  • Online:2018-12-01 Published:2018-12-01

摘要: 负荷预测是电力系统的一个传统研究问题。针对黑龙江省的气象和经济特点,提出了基于知识发现的负荷预测模型。首先通过传统的近大远小方法生成基本预测曲线,并根据从历史气象资料及负荷数据中提取的规则加以修正,生成最终预测曲线。该模型在黑龙江省电网公司得以应用,收到了较为理想的效果。

关键词: 荷预测,知识发现

Abstract: Load forecasting is a traditional research field of power system, this work made an analysis for meteorological and economic characteristics of Heilongjiang Province,and put forward the load forecasting model based on knowledge discovery. First of all, generate the initial prediction curve by traditional method, and extract related rules from the historical meteorological data and load data,update the initial prediction curve using these rules,to generate the final prediction curve. The model has been used in software system of load forecasting of Heilongjiang power grid Co. , Ltd, obtained desired results.

Key words: Load forecasting,Knowledge discovery

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