A short-term temperature forecaster based on a state space neural network

被引：18
|
作者：
Lanza, PAG
Cosme, JMZ
机构：
[1] Univ Valladolid, Syst & Automat Engn Dept, E-47005 Valladolid, Spain
[2] ICIMAF, Inst Cybernet Math & Phys, Havana, Cuba
来源：
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2002年 / 15卷 / 05期
关键词：
short-term temperature prediction; state space neural networks; electric load forecasting; random optimization methods;
D O I：
10.1016/S0952-1976(02)00089-1
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A short-term hourly environmental temperature forecaster for using in building electric load forecasting purposes has been designed based on a state space Neural Network (ssNN). The forecaster uses the current coded hour and the temperature as inputs, and predicts the next hour temperature. The training is based on a Random Optimization Method. Because of the non-stationary characteristic of temperature, training is executed daily in order to update the network weights. The dynamic of the outside temperature was satisfactorily captured by the ssNN when real data were used during several experiments. The encouraging results allow to use this predictor as a very good tool in Load Forecasting Systems. (C) 2003 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码：459 / 464
页数：6
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