TY - JOUR
T1 - Data-driven key performance indicators and datasets for building energy flexibility
T2 - A review and perspectives
AU - Li, Han
AU - Johra, H.
AU - Pereira, Flavia de Andrade
AU - Hong, Tianzhen
AU - Dreau, Jérôme Le
AU - Maturo, Anthony
AU - Wei, Mingjun
AU - Liu, Yapan
AU - Saberi-Derakhtenjani, Ali
AU - Nagy, Zoltan
AU - Marszal-Pomianowska, A.
AU - Finn, Donal
AU - Miyata, Shohei
AU - Kaspar, Kathryn
AU - Nweye, Kingsley
AU - O'Neill, Zheng
AU - Pallonetto, Fabiano
AU - Dong, Bing
PY - 2023/8/1
Y1 - 2023/8/1
N2 - Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 48 data-driven energy flexibility KPIs from 87 recent and relevant publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, standardizing data-driven energy flexibility quantification and verification processes; and (4) curating and analyzing datasets with proper description for energy flexibility assessments.
AB - Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 48 data-driven energy flexibility KPIs from 87 recent and relevant publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, standardizing data-driven energy flexibility quantification and verification processes; and (4) curating and analyzing datasets with proper description for energy flexibility assessments.
KW - physics.app-ph
KW - Building energy flexibility
KW - building-to-grid service
KW - demand flexibility
KW - demand response
KW - demand response datasets
KW - demand-side management
KW - grid-interactive buildings
KW - key performance indicator
KW - Key performance indicator
KW - Demand response
KW - Demand-side management
KW - Building-to-grid service
KW - Demand response datasets
UR - http://www.scopus.com/inward/record.url?scp=85156253327&partnerID=8YFLogxK
U2 - 10.1016/j.apenergy.2023.121217
DO - 10.1016/j.apenergy.2023.121217
M3 - Review article
SN - 0306-2619
VL - 343
JO - Applied Energy
JF - Applied Energy
M1 - 121217
ER -