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