Implementation of real-time model predictive heating control for a factory building using ANN-based lumped modelling approach.

It is important to control the heating system by following real-time demand, while considering the dynamic changes and non-uniform distributions of indoor environments. This paper presents a model predictive control (MPC) scheme for predicting indoor air temperatures at multiple points in a large factory building that consists of large irregular spaces and heat-generating equipment. Instead of using a full-blown dynamic simulation model (e.g. EnergyPlus), the authors developed a lumped simulation model. This model can accurately predict the temperatures and is, therefore, used for the optimal on/off control of 61 unit heaters installed in the factory building. Based on the MPC, energy savings of 56.3% were realized over three weeks, and the indoor air temperatures were maintained within a comfortable range. It is highlighted in the paper that this MPC approach based on the minimalistic lumped model can accurately predict indoor thermal behaviour and save significant energy. [ABSTRACT FROM AUTHOR]