## Abstract

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.

Original language | Swedish |
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Title of host publication | 1994 Annual Proceedings of the American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. |

Publisher | ASHRAE |

Pages | 1063-1074 |

Number of pages | 12 |

Publication status | Published - 1994 |

## Subject classification (UKÄ)

- Computational Mathematics