JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (6): 7-12.doi: 10.6040/j.issn.1672-3961.0.2017.530

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Nodal load forecasting method considering spatial correlation and redundancy

HAN Xueshan1, WANG Junxiong1, SUN Donglei2, LI Wenbo3, ZHANG Xinyi4, WEI Zhiqing5   

  1. 1. Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education(Shandong University), Jinan 250061, Shandong, China;
    2. Economic &
    Technology Research Institute, State Grid Shandong Electric Power Company, Jinan 250021, Shandong, China;
    3. Electric Power Research Institute, State Grid Shandong Electric Power Company, Jinan 250003, Shandong, China;
    4. State Grid Qingdao Power Supply Company, Qingdao 266002, Shandong, China;
    5. State Grid Yantai Power Supply Company, Yantai 264001, Shandong, China
  • Received:2017-09-21 Online:2017-12-20 Published:2017-09-21

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Abstract: Aiming at the problem that existing nodal load forecasting methods had no effective use for the nodes spatial correlation information, a new nodal load forecasting method with estimated correction characteristic was proposed, which had considered spatial correlation and redundancy. The correlation between the time dimension and the spatial dimension of the measurement information, and the spatial correlation and redundancy characteristics which combined these two dimensions were analyzed, and the mutual correction prediction principle was given. Two spatial correlations between state and measured values were analyzed deeply to establish measuring equations, which could characterise state features indirectly on the spatial correlation topology. Based on the analysis results, the forecasting model was established, and the forecasting method in which pre-prediction model was support vector machine was given, and advantages of the forecasting method were elaborated. Case studies demonstrated that compared with SVM model, the proposed 山 东 大 学 学 报 (工 学 版)第47卷 - 第6期韩学山,等:计及空间关联冗余的节点负荷预测方法 \=-method could effectively decrease forecasting errors and improve forecasting results.

Key words: support vector machine, redundant information, spatial correlation, state estimation, nodal load forecasting

CLC Number: 

  • TM315
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