Predicting System loads with Artificial Neural Networks: Method and Result from "the Great Energy Predictor Shootout"

Mattias Ohlsson, Carsten Peterson, Hong Pi, Thorsteinn Rögnvaldsson, Bo Söderberg

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

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.
Original languageSwedish
Title of host publication1994 Annual Proceedings of the American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc.
PublisherASHRAE
Pages1063-1074
Number of pages12
Publication statusPublished - 1994

Subject classification (UKÄ)

  • Computational Mathematics

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