Predicting Energy Requirement for Cooling the Building Using Artificial Neural Network

Rajesh Kumar; R. K. Aggarwal; J. D. Sharma; Sunil Pathania

doi:10.6000/1929-6002.2012.01.02.6

Authors

Rajesh Kumar Department of Physics, Shoolini University, Bajhol, Solan (HP) 173 212, India
R. K. Aggarwal Department of Environmental Science, Dr Y S Parmar University of Horticulture & Forestry, Nauni (Solan), 173230, India
J. D. Sharma Department of Physics, Shoolini University, Bajhol, Solan (HP) 173 212, India
Sunil Pathania Department of Engineering & Technology, Shoolini University, Bajhol, Solan (HP), India

DOI:

https://doi.org/10.6000/1929-6002.2012.01.02.6

Keywords:

Energy requirement, heat gain, ventilation losses, conduction losses, carbon emission, regression coefficient

Abstract

This paper explores total cooling load during summers and total carbon emissions of a six storey building by using artificial neural network (ANN). Parameters used for the calculation were conduction losses, ventilation losses, solar heat gain and internal gain. The standard back-propagation learning algorithm has been used in the network. The energy performance in buildings is influenced by many factors, such as ambient weather conditions, building structure and characteristics, the operation of sub-level components like lighting and HVAC systems, occupancy and their behavior. This complex situation makes it very difficult to accurately implement the prediction of building energy consumption. The calculated cooling load was 0.87 million kW per year. ANN application showed that data was best fit for the regression coefficient of 0.9955 with best validation performance of 0.41231 in case of conduction losses. To meet out this energy demand various fuel options are presented along with their cost and carbon emission.

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