Mattias Ohlsson
Professor
Predicting System loads with Artificial Neural Networks : Method and Result from "the Great Energy Predictor Shootout"
Author
Summary, in Swedish
We devise a feed-forward Artificial Neural Network (ANN) procedure for
predicting utility loads and present the resulting predictions for two
test problems given by ``The Great Energy Predictor Shootout - The First
Building Data Analysis and Prediction Competition''. Key ingredients in
our approach are a method ($\delta$ -test) for determining
relevant inputs and the Multilayer Perceptron. These methods are briefly
reviewed together with comments on alternative schemes like fitting to
polynomials and the use of recurrent networks.
predicting utility loads and present the resulting predictions for two
test problems given by ``The Great Energy Predictor Shootout - The First
Building Data Analysis and Prediction Competition''. Key ingredients in
our approach are a method ($\delta$ -test) for determining
relevant inputs and the Multilayer Perceptron. These methods are briefly
reviewed together with comments on alternative schemes like fitting to
polynomials and the use of recurrent networks.
Department/s
- Computational Biology and Biological Physics - Undergoing reorganization
Publishing year
1994
Language
Swedish
Pages
1063-1074
Publication/Series
1994 Annual Proceedings of the American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc.
Document type
Conference paper
Publisher
ASHRAE
Topic
- Computational Mathematics
Status
Published