Application of fuzzy multi-objective decision making in spatial load forecasting

被引:39
作者
Chow, MY [1 ]
Zhu, JX
Tram, H
机构
[1] N Carolina State Univ, Dept Elect & Comp Engn, Raleigh, NC 27695 USA
[2] ABB Power T&D Co Inc, Distribut Informat Syst, Cary, NC 27511 USA
关键词
spatial load forecasting; land-usage; urban redevelopment; fuzzy logic; Yager multi-objective decision making; information technology;
D O I
10.1109/59.709118
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Electric distribution system planning is to provide an economic expansion plan to meet the future demands in its territory. A forecast of the future electric demand and its geographic distribution is a prerequisite for distribution planning. The quality and accuracy of this forecast have large influence on the quality of the electrical distribution system planning. Spatial load forecasting emerges to provide a more accurate prediction of both the magnitudes and locations of future electric loads. Since the load growth pattern is dominated by its land-use (residential, commercial, or industrial), the land usage study of small area is important to capture the future loads accurately. There are many factors which will affect the customer land-use decision, for example, distance to highway, distance to urban pole, and the costs. The customer's preferences can be estimated based on these objective factors. Then the land utilization and the electricity consumption can be estimated. Since the objectives sometimes are conflicting each other, it can be cumbersome to use conventional cost function approach to determine the land usage decision. This paper will apply fuzzy multiobjective decision making scheme to the urban redevelopment and spatial load forecasting, which is more naturally and straight forward used to handle the spatial load forecasting problem. An example is used to illustrate the proposed methodology.
引用
收藏
页码:1185 / 1190
页数:6
相关论文
共 14 条
[1]  
[Anonymous], 1992, NEURAL NETWORKS FUZZ
[2]  
CHOW MY, 1996, IN PRESS IEEE T POWE
[3]  
CHOW MY, 1996, CRC PRESS IND ELECT
[4]  
CHOW MY, 1996, UNPUB IEEE T POWER S
[5]  
CHOW MY, 1995, CRC PRESS IND ELECT
[6]  
*ESRI, 1992, UND GIS
[7]  
Ross TJ., 2017, Fuzzy Logic with Engineering Applications, V4th ed.
[8]   COMPARISON TESTS OF 14 DISTRIBUTION LOAD FORECASTING METHODS [J].
WILLIS, HL ;
NORTHCOTEGREEN, JED .
IEEE TRANSACTIONS ON POWER APPARATUS AND SYSTEMS, 1984, 103 (06) :1190-1197
[9]  
WILLIS HL, 1983, IEEE T POWER SYSTEMS, V102, P1111
[10]  
WILLIS HL, 1994, INTRO INTEGRATED REW