Isam Shahrour

Prof. Isam Shahrour

Lille University
orcid

Research Focus & Metrics

Academic Career Length

36Years (1991-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 March 2008 in Tunnelling and Underground Space Technology
Verified
Journal Article
Closed access
FCF. Hage Chehade
F. Hage Chehade
Isam ShahrourIsam Shahrour
Isam Shahrour

10 January 2002 in International Journal for Numerical and Analytical Methods in Geomechanics
Verified
Journal Article
Closed access
PDF file
HMH. Mroueh
H. Mroueh
Isam ShahrourIsam Shahrour
Isam Shahrour

1 March 2013 in European Journal of Soil Biology
Verified
Journal Article
Closed access
ZZZhongqiu Zhao
Zhongqiu Zhao
Isam ShahrourIsam Shahrour
Isam Shahrour
ZBZhongke Bai
Zhongke Bai
WFWenxiang Fan
Wenxiang Fan
LFLiangrui Feng
Liangrui Feng
HLHuifeng Li
Huifeng Li

1 April 2003 in Computers and Geotechnics
Verified
Journal Article
Closed access
HMH. Mroueh
H. Mroueh
Isam ShahrourIsam Shahrour
Isam Shahrour

1 May 2010 in Mathematical and Computer Modelling
Verified
Journal Article
Closed access
RJRaed Jafar
Raed Jafar
Isam ShahrourIsam Shahrour
Isam Shahrour
IJIlan Juran
Ilan Juran

8 February 2018 in Energies
Verified
Journal Article
Open access
PDF file
NANivine Attoue
Nivine Attoue
Isam ShahrourIsam Shahrour
Isam Shahrour
RYRafic Younes
Rafic Younes
The smart building concept aims to use smart technology to reduce energy consumption, as well as to improve comfort conditions and users’ satisfaction. It is based on the use of smart sensors and software to follow both outdoor and indoor conditions for the control of comfort, and security devices for the optimization of energy consumption. This paper presents a data-based model for indoor temperature forecasting, which could be used for the optimization of energy device use. The model is based on an artificial neural network (ANN), which is validated on data recorded in an old building. The novelty of this work consists of the methodology proposed for the development of a simplified model for indoor temperature forecasting. This methodology is based on the selection of pertinent input parameters after a relevance analysis of a large set of input parameters, including solar radiation outdoor temperature history, outdoor humidity, indoor facade temperature, and humidity. It shows that an ANN-based model using outdoor and facade temperature sensors provides good forecasting of indoor temperatures. This model can be easily used in the optimal regulation of buildings’ energy devices.