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Detection and evaluation of heating load of building by machine learning

Khaled Mohamed Himair Swhli (University of Singidunum, Belgrade, Serbia)
Srdjan Jovic (Faculty of Technical Sciences, University of Priština, Kosovska Mitrovica, Serbia)
Nebojša Arsic (Faculty of Technical Sciences, University of Priština, Kosovska Mitrovica, Serbia)
Petar Spalevic (Faculty of Technical Sciences, University of Priština, Kosovska Mitrovica, Serbia)

Sensor Review

ISSN: 0260-2288

Article publication date: 1 December 2017

Issue publication date: 8 January 2018

Abstract

Purpose

This paper aims to explore detection of heating load of building by machine learning. Detection of heating load of building is very important in design of buildings due to efficient energy consumption.

Design/methodology/approach

In this study, detection of heating load of building based on effects of dry-bulb temperature, dew-point temperature, radiation, diffuse radiation and wind speed was analyzed. Machine learning approach was implemented for such a purpose.

Findings

The obtained results could be useful for future planning of heating load of buildings. Because the heating load of building is a very nonlinear phenomenon, it is suitable to use machine learning approach to avoid the nonlinearity of the system.

Originality/value

The obtained results could be used effectively in detection of heating load of buildings.

Keywords

Citation

Swhli, K.M.H., Jovic, S., Arsic, N. and Spalevic, P. (2018), "Detection and evaluation of heating load of building by machine learning", Sensor Review, Vol. 38 No. 1, pp. 99-101. https://doi.org/10.1108/SR-07-2017-0139

Publisher

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Emerald Publishing Limited

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