A data-driven workflow to improve energy efficient operation of commercial buildings: A review with real-world examples

Abstract

Data-driven building operation and maintenance research such as metadata inference, fault detection and diagnosis, occupant-centric controls (OCCs), and non-invasive load monitoring have emerged (NILM) as independent domains of study. However, there are strong dependencies between these domains; for example, quality of metadata affects the usability of fault detection and diagnostics techniques. Further, faults in controls hardware and programs limit the performance of OCCs. To this end, a literature review was conducted to identify the dependencies between these domains of research. Additionally, real-world examples using operational data from three institutional buildings in Ottawa, Canada, were provided and discussed to demonstrate these dependencies. Finally, a holistic tool-agnostic workflow was introduced which suggested the implementation of operational energy efficiency measures in the following order to ensure their full potential: (1) improve metadata, (2) address faults, (3) implement OCCs, and (4) monitor enhanced key performance indicators (KPIs). The proposed workflow is intended to be comprehensive, reproducible, nonintrusive, and inexpensive to implement. Practical applications: Optimization of building operations has been emerging among energy management professionals as a relatively low-cost means to achieve energy efficiency and minimize occupants’ discomfort. To this end, this study introduces a tool-agnostic data-driven workflow to building energy management practitioners that can assist them in achieving increased energy efficiency. The proposed workflow recognizes the interdependency of the various domains of research which have historically been treated independently.

FAIR compliant database development for human microbiome data samples

“…Machines can then more easily query the database for the right data and perform operations on it, while researchers, for example, can easily combine datasets to get new insights. This removes the friction that exists with different data formats and gives space for more efficient and faster data handling, which results in new insights to evolve more rapidly ( Abuimara et al., 2022 ). Large numerical datasets, such as data originating from multiscale simulation studies ( Sheraton et al., 2019 ; Béquignon et al., 2023 ), -omics ( Subramanian et al., 2020 ) or imaging studies ( Bray et al., 2017 ; Baglamis et al., 2023 ), should be properly categorized along with clearly described provenance.…”

Gamification Approaches and Assessment Methodologies for Occupants’ Energy Behavior Change in Buildings: A Systematic Review

“…The residential sector, serving as the primary energy consumer, allocates approximately 60% of the total final energy consumption for space heating, 25% for residential hot water and 11% for electricity across Europe and the United Kingdom [1,2]. perspective, significant investments have already been made in the improvement of building envelope elements [3][4][5], automation and control [6][7][8], sensing infrastructures [9,10] for relevant data provision and total or partial exchange of energy sources [11][12][13][14]. However, human involvement has emerged as a significant factor contributing to energy overconsumption [15][16][17].…”