Neural network predictive schemes for building temperature control: a comparative study

Abstract

Starting from an application of a real medium-size university building, the present paper focuses on the comparison among different ways to synthesize a predictive control scheme to improve the energy performance for heating, ventilation and air conditioning system of the building. The main motivation is the comparison among a nonlinear predictive control structure previously developed (based on first principle equations) with a predictive control whose prediction model is an artificial neural network. Particular emphasis is given on how to tune the neural network to gain good closed-loop performance. Twenty-one different networks are designed and tuned in order to correlate their closed-loop performance with the type and length of training data set, for building energy efficiency applications. Finally, a linear time-variant predictive control is given, obtained as analytical linearization along the future system trajectory, of the nonlinear equations of the neural network model. The goal is to add to the comparison a low computational burden (linear controller) still derived from nonlinear data-driven methods.