heating load; neural networks; delta-bar-delta; outdoor temperature predictor
Issue Date
2000-06
Publisher
제어로봇시스템학회
Citation
제어로봇시스템학회 논문지 2000, v. 6, no. 6, page. 441-446
Abstract
This paper presents an approach for the prediction of optimal heating load using a diagonal recurrent neural networks(DRNN) and data base system of outdoor temperature. In the DRNN, a dynamic backpropagation(DBP) with delta-bar-delta learning method is used to train an optimal heating load identifier. And the data base system is utilized for outdoor temperature prediction. Compared to other kinds of methods, the proposed method gives better prediction performance of heating load. Also a hardware for the controller is developed using a microprocessor. The experimental results show that prediction enhancement for heating load can be achieved with the proposed method regardless of the its inherent nonlinearity and large time constant.