An artificial neural network model for predicting building heating and cooling loads

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Citations (Scopus)

Abstract

In this work, an artificial neural network is designed for predicting the heating and cooling loads for buildings. The paper develops two neural models that make use of a dataset with 8 input attributes with the output as a numeric value of the heating and cooling loads of the buildings. The predictive abilities of the neural nets are compared with linear regression under conventional validation, 5-fold validation and 10-fold validation. Obtained results show that both neural models obtain encouraging results and can be dependably deployed in building loads determination.

Original languageEnglish
Title of host publicationIDAP 2017 - International Artificial Intelligence and Data Processing Symposium
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538618806
DOIs
Publication statusPublished - 30 Oct 2017
Event2017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017 - Malatya, Turkey
Duration: 16 Sept 201717 Sept 2017

Publication series

NameIDAP 2017 - International Artificial Intelligence and Data Processing Symposium

Conference

Conference2017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017
Country/TerritoryTurkey
CityMalatya
Period16/09/1717/09/17

Keywords

  • Artificial neural networks
  • Cooling loads
  • Heating loads
  • Linear regression

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Signal Processing

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