Investigating the occupant existence to reduce energy consumption by using a hybrid artificial neural network with metaheuristic algorithms

There is an acute need to evaluate the energy consumption of buildings in response to climate change. The “occupant” factor has been largely overlooked in building energy analysis. This research aims at investigating occupancy existence in the office environment using a hybrid artificial ne...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autor principal:	Nehal Elshaboury
Formato:	article
Lenguaje:	EN
Publicado:	Growing Science 2022
Materias:	Analysis QA299.6-433 Business mathematics. Commercial arithmetic. Including tables, etc. HF5691-5716
Acceso en línea:	https://doaj.org/article/a7ee7245afdb4cc3a757b9df44c4e2e0
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a7ee7245afdb4cc3a757b9df44c4e2e0
record_format	dspace
spelling	oai:doaj.org-article:a7ee7245afdb4cc3a757b9df44c4e2e02021-11-12T06:26:21ZInvestigating the occupant existence to reduce energy consumption by using a hybrid artificial neural network with metaheuristic algorithms1929-58041929-581210.5267/j.dsl.2021.8.001https://doaj.org/article/a7ee7245afdb4cc3a757b9df44c4e2e02022-01-01T00:00:00Zhttp://www.growingscience.com/dsl/Vol11/dsl_2021_29.pdfhttps://doaj.org/toc/1929-5804https://doaj.org/toc/1929-5812 There is an acute need to evaluate the energy consumption of buildings in response to climate change. The “occupant” factor has been largely overlooked in building energy analysis. This research aims at investigating occupancy existence in the office environment using a hybrid artificial neural network with metaheuristic algorithms for improved energy management. It proposes and compares three classification models, namely particle swarm optimization (PSO), gravitational search algorithm (GSA), and hybrid PSO-GSA in combination with the feedforward neural network (FFNN). The inputs to these models are data related to temperature, humidity, light, and carbon dioxide emissions. Two data sets are used for testing the models while the office door is open and closed. The capabilities of the optimized models are evaluated using best, average, median, and standard deviation of the mean squared error. Most of the performance metrics indicate that the FFNN-PSO-GSA model exhibits better performance compared to the other models using the two datasets. The proposed model yields a classification accuracy ranging between 98.47-98.73% using one predictor (i.e., temperature). Besides, it yields an accuracy ranging between 85.45-94.03% using temperature and CO2 predictors. It can be concluded that the FFNN combined with PSO and GSA algorithms can be a useful tool for occupancy detection modeling.Nehal ElshabouryGrowing SciencearticleAnalysisQA299.6-433Business mathematics. Commercial arithmetic. Including tables, etc.HF5691-5716ENDecision Science Letters, Vol 11, Iss 1, Pp 91-104 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Analysis QA299.6-433 Business mathematics. Commercial arithmetic. Including tables, etc. HF5691-5716
spellingShingle	Analysis QA299.6-433 Business mathematics. Commercial arithmetic. Including tables, etc. HF5691-5716 Nehal Elshaboury Investigating the occupant existence to reduce energy consumption by using a hybrid artificial neural network with metaheuristic algorithms
description	There is an acute need to evaluate the energy consumption of buildings in response to climate change. The “occupant” factor has been largely overlooked in building energy analysis. This research aims at investigating occupancy existence in the office environment using a hybrid artificial neural network with metaheuristic algorithms for improved energy management. It proposes and compares three classification models, namely particle swarm optimization (PSO), gravitational search algorithm (GSA), and hybrid PSO-GSA in combination with the feedforward neural network (FFNN). The inputs to these models are data related to temperature, humidity, light, and carbon dioxide emissions. Two data sets are used for testing the models while the office door is open and closed. The capabilities of the optimized models are evaluated using best, average, median, and standard deviation of the mean squared error. Most of the performance metrics indicate that the FFNN-PSO-GSA model exhibits better performance compared to the other models using the two datasets. The proposed model yields a classification accuracy ranging between 98.47-98.73% using one predictor (i.e., temperature). Besides, it yields an accuracy ranging between 85.45-94.03% using temperature and CO2 predictors. It can be concluded that the FFNN combined with PSO and GSA algorithms can be a useful tool for occupancy detection modeling.
format	article
author	Nehal Elshaboury
author_facet	Nehal Elshaboury
author_sort	Nehal Elshaboury
title	Investigating the occupant existence to reduce energy consumption by using a hybrid artificial neural network with metaheuristic algorithms
title_short	Investigating the occupant existence to reduce energy consumption by using a hybrid artificial neural network with metaheuristic algorithms
title_full	Investigating the occupant existence to reduce energy consumption by using a hybrid artificial neural network with metaheuristic algorithms
title_fullStr	Investigating the occupant existence to reduce energy consumption by using a hybrid artificial neural network with metaheuristic algorithms
title_full_unstemmed	Investigating the occupant existence to reduce energy consumption by using a hybrid artificial neural network with metaheuristic algorithms
title_sort	investigating the occupant existence to reduce energy consumption by using a hybrid artificial neural network with metaheuristic algorithms
publisher	Growing Science
publishDate	2022
url	https://doaj.org/article/a7ee7245afdb4cc3a757b9df44c4e2e0
work_keys_str_mv	AT nehalelshaboury investigatingtheoccupantexistencetoreduceenergyconsumptionbyusingahybridartificialneuralnetworkwithmetaheuristicalgorithms
_version_	1718431189806612480

Investigating the occupant existence to reduce energy consumption by using a hybrid artificial neural network with metaheuristic algorithms

Ejemplares similares