SHORT-TERM LOAD FORECASTING USING NEURAL NETWORKS

被引：58

作者：

KIARTZIS, SJ

BAKIRTZIS, AG

PETRIDIS, V

机构：

[1] Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Salonika

来源：

ELECTRIC POWER SYSTEMS RESEARCH | 1995年 / 33卷 / 01期

关键词：

LOAD FORECASTING; NEURAL NETWORKS;

D O I：

10.1016/0378-7796(95)00920-D

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 (ANN) model for short-term load forecasting (STLF) is presented. The proposed model is capable of forecasting the next 24-hour load profile at one time, as opposed to the usual 'next one hour' ANN models. The inputs to the ANN are load profiles of the two previous days and daily maximum and minimum temperature forecasts. The network is trained to learn the next day's load profile. Testing of the model with one year of data from the creek interconnected power system resulted in a 2.66% average absolute forecast error.

引用

页码：1 / 6

页数：6

共 19 条

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