Ming Jin (U.C. Berkeley); Ruoxi Jia (UC Berkeley); Hari Prasanna Das (UC Berkeley); Wei Feng (Lawrence Berkeley National Laboratory); Costas J. Spanos (University of California at Berkeley)
Energy use in buildings account for approximately half of global electricity consumption and a significant amount of CO2 emissions. Often, the higher energy usage in buildings are accounted to old and poorly maintained infrastructure and equipments. On the other hand, Smart buildings are capable of achieving energy efficiency by using intelligent services such as indoor positioning, personalized lighting, demand-based heating ventilation and air-conditioning, automatic fault detection and recovery etc. However, most buildings nowadays lack the basic components and infrastructure to support such services. The investment decision of intelligent system design and retrofit can be a daunting task, because it involves both hardware (sensors, actuators, servers) and software (operating systems, service algorithms), which have issues of compatibility, functionality constraints, and opportunities of co-design of synergy. Our work proposes a user-oriented investment decision toolset using optimization and machine learning techniques aimed at handling the complexity of exploration in the large design space and to enhance cost-effectiveness, energy efficiency, and human-centric values. The toolset is demonstrated in a case study to retrofit a medium-sized building, where it is shown to propose a design that significantly lowers the overall investment cost while achieving user specifications.