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

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

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Abstract

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
PublisherIEEE
ISBN (Electronic)9781509046232
ISBN (Print)9781509046249
DOIs
Publication statusPublished - 29 May 2017

Publication series

Name
ISSN (Print)2472-9647

Fingerprint

Neural networks
Virtual reality
Heating
Mean square error
Atmospheric humidity
Moisture
Energy utilization
Electricity
Cooling
Temperature
Air
Experiments

Keywords

  • random neural network

Cite this

Ahmad, Jawad ; Larijani, Hadi ; Emmanuel, Rohinton ; Mannion, Mike ; Javed, Abbas ; Phillipson, Mark. / Energy demand prediction through novel random neural network predictor for large non-domestic buildings. 2017 Annual IEEE International Systems Conference (SysCon). Montreal, QC, Canada : IEEE, 2017.
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title = "Energy demand prediction through novel random neural network predictor for large non-domestic buildings",
abstract = "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.",
keywords = "random neural network",
author = "Jawad Ahmad and Hadi Larijani and Rohinton Emmanuel and Mike Mannion and Abbas Javed and Mark Phillipson",
note = "Acceptance in SAN Exception form sent 14/11/19 ET",
year = "2017",
month = "5",
day = "29",
doi = "10.1109/SYSCON.2017.7934803",
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Energy demand prediction through novel random neural network predictor for large non-domestic buildings. / Ahmad, Jawad; Larijani, Hadi; Emmanuel, Rohinton; Mannion, Mike; Javed, Abbas; Phillipson, Mark.

2017 Annual IEEE International Systems Conference (SysCon). Montreal, QC, Canada : IEEE, 2017.

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

TY - GEN

T1 - Energy demand prediction through novel random neural network predictor for large non-domestic buildings

AU - Ahmad, Jawad

AU - Larijani, Hadi

AU - Emmanuel, Rohinton

AU - Mannion, Mike

AU - Javed, Abbas

AU - Phillipson, Mark

N1 - Acceptance in SAN Exception form sent 14/11/19 ET

PY - 2017/5/29

Y1 - 2017/5/29

N2 - 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.

AB - 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.

KW - random neural network

U2 - 10.1109/SYSCON.2017.7934803

DO - 10.1109/SYSCON.2017.7934803

M3 - Conference contribution

SN - 9781509046249

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PB - IEEE

CY - Montreal, QC, Canada

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