Optimal maintenance planning in building retrofitting with interacting energy effects

Abstract

Summary This paper extends a control system framework for the maintenance planning investment decision for building energy retrofitting. The interacting energy and reliability effects that are ignored by previous models are incorporated in the current study. A set of energy efficiency and population decay models with interacting parameters and decision variables are established, and a state‐space model with coupled nonlinear equations is obtained. The control objectives are maximizing the energy savings and financial paybacks with limited budget during a finite time period. An model predictive control (MPC) controller is designed for the problem. The interacting effects and effectiveness of the proposed approach are verified by the case study, where improvements from the modeling considering interaction are revealed.

Maintenance strategies and energy efficiency: a review

“…In addition, the optimal maintenance model has been proposed for lighting and air conditioner retrofit. One study proposed optimal maintenance planning for air conditioners and lighting retrofitting considering their interaction (Wang et al ., 2020). The nonlinear decay model was used for lighting, and Weibull distribution was used for air conditioners.…”

Control for hybrid systems: Applications and methods for adaptation and optimality

“…New stability conditions and control designs based on numerical methods, including linear matrix inequalities and gradient based optimization methods, are introduced to address time delays, parametric uncertainties, and the nonlinearities involved in the switching dynamics with stability and performance guarantees. The second group of articles, 5‐14 constituting the major category of this issue, applies adaptive control, optimization methods, and hybrid system theories to various practical fields, ranging from UAVs, AGVs, robotics, and water systems to batch processes. The last group 15,16 considers distributed cyber physical systems, which deals with parametric uncertainties, communication constraints, and signal estimations by resorting to adaptive control and fast Kalman filtering.…”