Urban Building Energy CPS (UBE-CPS): Real-Time Demand Response Using Digital Twin

Abstract

Cities are facing unprecedented growth with an increase in population and urbanization. The United Nations estimates that the global population will increase to 9.3 billion by 2050, which is an increase of 30% compared to the population in 2011 (UN, 2015). As development in dense urban areas continues, the scientific community must continue to observe, analyze, and interpret the effects of dense urbanization, including climate change impacts on urban sustainability, particularly buildings. The city governments are gradually modifying their policies, decisions, and strategies towards green and energy efficient approaches. Particularly, decisions related to expanding energy generation facilities are critical. Additionally, cities need to manage their energy consumption, now and in future, as they move toward a time variable sources of renewable energy such as solar and wind. In this chapter, we discuss the development of a novel Urban Building Energy CPS (UBE-CPS) framework that bridges the physical and the digital world through seamless data transfer for real-time demand response. While the data from physical world relates to sensor data obtained from buildings, the digital world is the Digital Twin, an advanced machine-learning model that is coupled with urban-scale EnergyPlus™ models that represent individual buildings. Through a feedback loop to the City Utility Manager / Administrator, UBE-CPS will become an integral part of the city energy management to automate and, potentially, control building-level demand curve to satisfy grid-level demand response.