A smart building semantic platform to enable data re-use in energy analytics applications: the Data Clearing House.
CoRR(2023)
摘要
Systems in the built environment continuously emit time series data about
resource usage (e.g., energy and water), embedded electrical
generation/storage, status of equipment, patterns of building occupancy, and
readings from IoT sensors. This presents opportunities for new analytics and
supervisory control applications that help reduce greenhouse gas emissions due
to energy demand, if the barrier of data heterogeneity can be overcome.
Semantic models of buildings -- representing structure, integrated equipment,
and the many internal connections -- can help achieve interoperable data re-use
by describing overall context, in addition to metadata. In this paper, we
describe the Data Clearing House (DCH), a semantic building platform that hosts
sensor data, building models, and analytics applications. This fulfills the key
phases in the lifecycle of semantic building data, which includes:
cost-effective ingestion of Building Management System (BMS), IoT, metering and
meteorological time series data from a wide range of open and proprietary
systems; importing and validating semantic models of sites and buildings using
the Brick Schema; interacting with a discovery API via a high-level
domain-specific query language; and deploying applications to modelled
buildings. Having onboarded multiple buildings belonging to our own
organisation and external partners, we are able to comment on the challenges to
success of this approach. As an example use-case of the semantic building
platform, we describe a measurement and verification (M&V) application
implementing the 'whole facility' (Option C) method of the International
Performance Measurement and Verification Protocol (IPMVP) for evaluating
electrical metering data. This compares energy consumption between nominated
baseline and analysis time periods, to quantify the energy savings achieved
after implementing an intervention on a site.
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