A Comprehensive Review of Digital Twin Technology in Building Energy Consumption Forecasting

Abstract

With the global rise in urban populations, energy consumption in buildings has become a critical issue, now accounting for about 30% of total global energy use. Developing powerful energy forecasting systems is challenging due to frequent fluctuations in energy demand. The digitalization of building energy forecasting systems, enhanced by Energy Digital Twin technology alongside IoT devices and advanced data-driven algorithms, offers substantial improvements in energy management and optimization, servicing, maintenance, and energy-efficient design. This paper not only presents a literature evaluation categorizing the applications of digital twins in energy consumption forecasting but also conducts a thorough review of digital twin architecture and existing energy forecasting models through a systematic literature review approach. This evaluation enables the classification of studies into areas such as overall energy consumption prediction, HVAC system performance, and indoor air quality improvement, furthering the pursuit of net-zero and positive energy buildings as well as more effective energy systems. Furthermore, the findings and discussions presented in this paper potentially initiate future perspectives in developing a powerful digital twin system for energy forecasting in buildings and underscore the need for further research to address existing gaps and enhance the development of digital twins in building energy management, thereby meeting the sector's dynamic needs and contributing to global sustainability efforts.