Creating Efficient Building Energy Management Systems from Room-Level Big Data
This study describes the first step of a scalable, bottom-up approach to developing energy-saving initiatives at the individual level
Reducing global energy consumption is key to our battle against climate change. A major step towards this is energy conservation in buildings. But current building energy management strategies focus on energy consumption at a macro-scale, which does not allow for occupant-led modifications to energy consumption. Recently, researchers from Incheon National University proposed a novel strategy that employs real-time big data to identify occupant energy usage patterns. The new technique can be scaled up to the community level.
The first step in the development of energy-efficient benchmarks for existing buildings is the accurate measurement of the energy consumption patterns. This usually involves data analysis using large volumes of data to understand the space and time distribution patterns of energy use. For example, total energy usage depends on the types of electrical appliances used, such as the heating and cooling systems, as well as their monthly, daily, and hourly usage patterns. Annual data on the energy use of buildings is the foundation for the estimation of community-level energy rating systems. However, it is inadequate to determine energy-savings at the smallest level, which is the occupant-level. A room-level energy calculator based on real-time energy usage data, which can be scaled up to the building- and the community-level, is required to fulfil this need.
In a study published in Renewable and Sustainable Energy Reviews, a research team led by Professor Choongwan Koo from Incheon National University, Korea, has proposed a process model for the calculation of typical room-level energy usage patterns. Speaking of the research, Professor Koo says, “We have developed a robust model that can meet the challenges of accurate occupant-level measurements, including variations in space, time and appliances/equipment.”
In this study, the research team selected an educational facility in Sangju, Korea as a sample building and equipped every classroom with real-time internet-of-things (IoT) energy sensors. They also corrected data directly from electrical meters in the school. This constituted the “big data” of approximately 11 million datasets that was then used for analysis. The new method aimed to consider energy consumption from three perspectives — from the space unit based on occupant perception, the time unit, which allows rapid response from occupants, and the appliance-level or equipment unit.
The researchers then used the big data to identify representative energy consumption patterns using a “k-clustering algorithm.” Furthermore, they analyzed these patterns and used them to develop scalable energy benchmarks for various components of the energy system, such as lighting, educational devices, cooling, heating, and more. Next, the research team calculated the mean absolute percentage error (MAPE) to validate these energy benchmarks. It was also used to calculate the uncertainty of the benchmarks, which allows insights into how certain features, such as the seasonal weather and hour-by-hour usage patterns affect energy consumption.
“The scalable energy benchmarks can help in the planning of effective operational strategies to boost energy savings. It also improves energy efficiency and Indoor Environmental Quality (IEQ). For example, during periods of peak consumption, like summer afternoons and winter mornings, different strategies for saving energy on cooling and heating systems could be applied. Likewise, methods to turn off the standby power when the facility is not in use can be developed,” explains Professor Koo.
These techniques will be invaluable in the development of smart building energy performance rating systems and in creating more comfortable workspaces. They will also enable occupants at the individual level to incorporate changes in the way they use energy, thereby leading to an overall reduction in energy consumption.
Reference
DOI: https://doi.org/10.1016/j.rser.2022.112944
Title of original paper: A novel process model for developing a scalable room-level energy benchmark using real-time bigdata: Focused on identifying representative energy usage patterns
Journal: Renewable and Sustainable Energy Reviews
Authors: Junsoo Lee, Tae Wan Kim, Choongwan Koo
Affiliations:
Division of Architecture & Urban Design, Incheon National University, Incheon, 22012, Republic of Korea
*Corresponding author’s email: cwkoo@inu.ac.kr
About Incheon National University
Incheon National University (INU) is a comprehensive, student-focused university. It was founded in 1979 and given university status in 1988. One of the largest universities in South Korea, it houses nearly 14,000 students and 500 faculty members. In 2010, INU merged with Incheon City College to expand capacity and open more curricula. With its commitment to academic excellence and an unrelenting devotion to innovative research, INU offers its students real-world internship experiences. INU not only focuses on studying and learning but also strives to provide a supportive environment for students to follow their passion, grow, and, as their slogan says, be INspired.
Website: http://www.inu.ac.kr/mbshome/mbs/inuengl/index.html
About the author
Professor Choongwan Koo obtained his Ph.D. degree in the field of Sustainable Construction Engineering & Management from Yonsei University in 2014. After joining as an Assistant Professor at the Department of Building Services Engineering, at the Hong Kong Polytechnic University in 2016, he focused on the field of intelligent facility management and smart construction management. Since September 2019, he has been working in the Division of Architecture & Urban Design at Incheon National University. He works on smart construction management and intelligent facility management as a director of research projects funded by government agencies such as National Research Foundation.
