Vol 6, No 6 (2015) > Mechanical Engineering >

Heating Load Predictions using The Static Neural Networks Method

S. Sholahudin, Hwataik Han



Heating load calculations are essential to optimize energy use in
buildings during the winter season. Instantaneous heating loads are determined
by the outdoor weather conditions. It is intended to develop a method to
predict instantaneous building heating loads, depending on various combinations
of current input parameters so as to apply HVAC equipment operations. Heating
loads have been calculated in a representative apartment building for one month
in Seoul using Energy Plus. The datasets obtained are used to train artificial
neural networks. Dry bulb temperature, dew point temperature, global horizontal
radiation, direct normal radiation and wind speed are selected as the input
parameters for training, while heating loads are the output. The design of  experiments is used to investigate the effect
of individual input parameters on the heating loads. The results of this study
show the feasibility of using a machine learning technique to predict
instantaneous heating loads for optimal building operations.

Keywords: Building; Energy; Heating loads; Neural networks, Orthogonal array

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