Prediction of Heat Energy Consumption by LSTM Sequence- to-Sequence Models

Rozina Mohaideen , Mazen Ossman, Y Bi

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

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Abstract

The accurate estimation of heat energy performance in buildings is critical for optimizing energy demand and supply. Non-residential properties have predictable operating patterns in principle, incorporating these patterns into simulations of energy consumption can help estimate building energy use. In this work we develop Long-Short Term Memory (LSTM) Sequence to Sequence and Gated Recurrent Unit (GRU) architectures, which are composed of Dropout, Re- peat Vector, Time-distributed and Graph Convolution layers. We have conducted a rigor comparative study on the structures and hyper parameters using the na- tional grid data, then use the learnt models for the energy demand site manage- ment undertaken in a laboratory environment.
Original languageEnglish
Title of host publication12th International Advanced Computing Conference on 16th & 17th Dec'22 @ CMR College of Engineering & Technology, Hyderabad, Telangana
PublisherSpringer
Pages1-14
Number of pages14
Publication statusPublished (in print/issue) - 16 Dec 2022
Event12th International Advanced Computing Conference on 16th & 17th Dec'22 - CMR College of Engineering & Technology , , Hyderabad, Telangana, India
Duration: 16 Dec 202217 Jan 2023
https://computingconf.com

Conference

Conference12th International Advanced Computing Conference on 16th & 17th Dec'22
Country/TerritoryIndia
City, Hyderabad, Telangana
Period16/12/2217/01/23
Internet address

Keywords

  • LSTM
  • GRU
  • energy consumption prediction

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