Hierarchical Bayesian neural nets for air-conditioning load prediction: nonlinear dynamics approach

Author

Nakajima, Y. ; Sugi, J. ; Saito, M. ; Hamagishi, H. ; Hattori, D. ; Matsumoto, T.

Author_Institution

Dept. of Electr. Electron. & Comput. Eng., Waseda Univ., Tokyo, Japan

Volume

3

fYear

1998

fDate

4-9 May 1998

Firstpage

1948

Abstract

Given time series data, model dynamical systems are built using a hierarchical Bayesian scheme with feedforward neural nets and then the models are compared in terms of marginal likelihood. The model with the highest marginal likelihood is used for predictions. The algorithm is applied to building air-conditioning load prediction

Keywords

Bayes methods; air conditioning; feedforward neural nets; load forecasting; nonlinear dynamical systems; thermal energy storage; time series; air-conditioning load prediction; feedforward neural nets; hierarchical Bayesian neural nets; marginal likelihood; nonlinear dynamics approach; thermal energy storage; time series data; Bayesian methods; Distributed computing; Energy storage; Feedforward neural networks; Neural networks; Nonlinear dynamical systems; Power generation; Power system modeling; Prediction algorithms; Predictive models;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on

Conference_Location

Anchorage, AK

ISSN

1098-7576

Print_ISBN

0-7803-4859-1

Type

conf

DOI

10.1109/IJCNN.1998.687157

Filename

687157