Energy demand prediction through novel random neural network predictor for large non-domestic buildings

Research output: Chapter in Book/Report/Conference proceedingConference contribution

27 Citations (Scopus)
144 Downloads (Pure)


Buildings are among the largest consumers of energy in the world. In developed countries, buildings currently consumes 40% of the total energy and 51% of total electricity consumption. Energy prediction is a key factor in reducing energy wastage. This paper presents and evaluates a novel RNN technique which is capable to predict energy utilization for a non-domestic large building comprising of 562 rooms. Initially, a model for the 562 rooms is developed using Integrated Environment Solutions Virtual Environment (IES-VE) software. The IES-VE model is simulated for one year and 10 essential data inputs i.e., air temperature, dry resultant temperature, internal gain, heating set point, cooling set point, plant profile, relative humidity, moisture content, heating plant sensible load, internal gain and number of people are measured. Datasets are generated from the measured data. RNN model is trained with this datasets for the energy demand prediction. Experiments are used to identify the accuracy of prediction. The results show that the proposed RNN based energy model achieves 0.00001 Mean Square Error (MSE) in just 86 epochs via Gradient Decent (GD) algorithm.
Original languageEnglish
Title of host publication2017 Annual IEEE International Systems Conference (SysCon)
Place of PublicationMontreal, QC, Canada
ISBN (Electronic)9781509046232
ISBN (Print)9781509046225
Publication statusPublished - 29 May 2017

Publication series

ISSN (Print)2472-9647


  • IES-VE and building simulation
  • non-domestic building
  • random neural network
  • energy demand prediction
  • optimizations

ASJC Scopus subject areas

  • Artificial Intelligence
  • Instrumentation
  • Control and Systems Engineering
  • Hardware and Architecture


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