Artificial Intelligence for Advanced Building Control: Energy and GHG Savings from a Case Study

Abstract

Model-based Predictive Control (MPC) is a promising advanced control strategy for the improvement of building operation. MPC uses a model of the building along with weather forecasts to optimize control strategies, such as indoor air temperature set-points, thermal storage charging and discharging cycles, etc. An obstacle to the adoption of MPC is the modelling step: developing a dedicated control-oriented model is a time-consuming process, requiring technical expertise and a large amount of information about the building and its operation. To overcome these issues, this paper proposes a new approach for the development of MPC strategies based on Artificial Intelligence (AI) techniques, aiming to map correlations among commonly available operation variables and to develop models suitable for predictive control. The proposed approach was applied in an institutional building in Varennes, QC, with the aim of reducing the natural gas consumption during the heating season. Early results show a remarkable effectiveness of the proposed approach, with a reduction of natural gas and building heating consumption of 23.9% and 6.3%, respectively.

A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings

“…A mini-pilot program was conducted using about 10 users to confirm the effectiveness of their approach. In a bid to address the challenge of using model-based predictive control (MPC) as an advanced control strategy, Cotrufo et al [77] proposed a novel approach by developing an AI-based MPC using commonly available variables. They applied this approach to a building in Varennes, Quebec, for the reduction of natural gas use for the heating season.…”