Simulation and optimal control of heating and cooling systems: a case study of a commercial building

TL;DR

This paper presents a model-based predictive control method for optimizing heating and cooling in commercial buildings, achieving significant energy savings while maintaining comfort.

Contribution

It introduces a grey-box model for building heat load prediction and an optimization strategy that reduces energy use without changing existing regulation modes.

Findings

Up to 12% energy savings achieved in case study.

Mean power forecast error of 8%.

Effective in both heating and cooling seasons.

Abstract

In this paper, an energy conservation measure that optimizes the planning of heating and cooling systems for tertiary sector buildings is proposed. It consists of a model-based predictive control approach that employs a grey-box model built from the building data history and from weather condition data that predicts the building heat load and indoor temperature. This model is then used by heating and cooling optimization strategies that aim at reducing the total energy consumption of the building in the next day while satisfying the desired indoor thermal comfort constraint. The proposed optimization strategies do not modify the regulation mode in place; rather, they send optimized set-points to the building management system in order to reduce the energy consumption. We applied our approach in a case study of a commercial building during heating and cooling seasons and we show that it was able to yield up to 12% of energy savings while having a mean power forecast error of 8%.