'Smart' energy systems and networked buildings : examining the integrations, controls, and experience of design through operation

• Main Author: Fedoruk, Laura Elizabeth
• Language: English
• Published: University of British Columbia 2013
• Online Access: http://hdl.handle.net/2429/45658

Designs for new infrastructure such as buildings and energy systems often have the goals of being ‘smart’ and ‘sustainable’. These goals often coincide with designs that integrate renewable and distributed energy systems, industrial ecology based principles, increased controls and monitoring capabilities, and integrated design techniques. This thesis attempts to understand the design and process-based lessons that help to achieve these goals in networked infrastructure through the use of a contextual literature review as well as two case studies that examine the design and early operation of the networked energy and controls systems at the University of British Columbia’s (UBC) Centre for Interactive Research on Sustainability (CIRS). The thesis examines various literatures associated with ‘smart’ and ‘sustainable’ systems inclusive of sustainable buildings, smart grids, distributed energy systems, and industrial ecology and finds that learning processes and systems integration are key to the success of these projects. From this understanding, a case study is used to examine the energy systems at CIRS in order to understand the systems integration of energy infrastructure during early operation. The analysis reveals that in order to create systems that meet their design intent and create symbiotic relationships within a network, it is paramount to understand system boundaries and network effects throughout the lifecycle of a project – from design through to operation and optimization. A second case study examines the systems at CIRS that are usually considered the ‘smart’ component of infrastructure, controls and monitoring capabilities, and finds that in order to have successful controls systems it is necessary to design and operate these systems in a way that complements the human systems that interact with them. Designing for learning enables operator troubleshooting processes and inhabitant feedback and understanding.