  This paper presents the building heating demand prediction model with
occupancy profile and operational heating power level characteristics in short
time horizon (a couple of days) using artificial neural network. In addition,
novel pseudo dynamic transitional model is introduced, which consider time
dependent attributes of operational power level characteristics and its effect
in the overall model performance is outlined. Pseudo dynamic model is applied
to a case study of French Institution building and compared its results with
static and other pseudo dynamic neural network models. The results show the
coefficients of correlation in static and pseudo dynamic neural network model
of 0.82 and 0.89 (with energy consumption error of 0.02%) during the learning
phase, and 0.61 and 0.85 during the prediction phase respectively. Further,
orthogonal array design is applied to the pseudo dynamic model to check the
schedule of occupancy profile and operational heating power level
characteristics. The results show the new schedule and provide the robust
design for pseudo dynamic model. Due to prediction in short time horizon, it
finds application for Energy Services Company (ESCOs) to manage the heating
load for dynamic control of heat production system.
