POWER-DEMAND FORECASTING USING A NEURAL-NETWORK WITH AN ADAPTIVE LEARNING ALGORITHM

被引：13

作者：

DASH, PK ^{[1
]}

LIEW, AC ^{[1
]}

RAMAKRISHNA, G ^{[1
]}

机构：

[1] REG ENGN COLL,ROUKELA 769008,INDIA

来源：

IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION | 1995年 / 142卷 / 06期

关键词：

D O I：

10.1049/ip-gtd:19952245

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：

An artificial neural network with an adaptive-Kalman-filter-based learning algorithm is presented for forecasting weather-sensitive loads. The proposed model can differentiate between weekday and weekend loads, This neural-network model has been implemented using real load data, The results reveal the efficiency and accuracy of the proposed approach in terms of short learning time, rapid convergence and the adaptive nature of the learning algorithm.

引用

页码：560 / 568

页数：9

共 14 条

[1]

BUNN DW, 1989, COMP MODELS ELECTRIC

[2]

DASH PK, 1993, ENG INT SYSTEMS, V1, P103

[3]

ELSHARKAWI MA, 1991, 1ST INT FOR APPL NEU, P3

[4] SHORT-TERM LOAD FORECASTING [J].