Marcus M. Keane

Linking building data in the cloud: Integrating cross-domain building data using linked data

Within the operational phase buildings are now producing more data than ever before, from energy usage, utility information, occupancy patterns, weather data, etc. In order to manage a building holistically it is important to use knowledge from across these information sources. However, many barriers exist to their interoperability and there is little interaction between these islands of information. As part of moving building data to the cloud there is a critical need to reflect on the design of cloud-based data services and how they are designed from an interoperability perspective. If new cloud data services are designed in the same manner as traditional building management systems they will suffer from the data interoperability problems. Linked data technology leverages the existing open protocols and W3C standards of the Web architecture for sharing structured data on the web. In this paper we propose the use of linked data as an enabling technology for cloud-based building data services. The objective of linking building data in the cloud is to create an integrated well-connected graph of relevant information for managing a building. This paper describes the fundamentals of the approach and demonstrates the concept within a Small Medium sized Enterprise (SME) with an owner-occupied office building.

Towards a wireless sensor platform for energy efficient building operation

Abstract Currently, the IT-support for energy performance rating of buildings is insufficient. So-called IT-platforms often “built” of an ad-hoc, inconsistent combination of off-the-shelf building management components, distributed data metering equipment and several monitoring software tools. A promising approach to achieve consistent, holistic performance data management is the implementation of an integrated, modular wireless sensor platform. This paper presents an approach of how wireless sensors can be seamlessly integrated into existing and future intelligent building management systems supporting improved building performance and diagnostics with an emphasis on energy management.

Ontology-based facility data model for energy management

Context: Definition of a comprehensive facility data model is a prerequisite for providing more advanced energy management systems capable of tackling the underlying heterogeneity of complex infrastructures, thus providing more flexible data interpretation and event management, advanced communication and control system capabilities. Objective: This paper proposes one of the possible implementations of a facility data model utilizing the concept of ontology as part of the contemporary Semantic Web paradigm. Method: The proposed facility ontology model was defined and developed to model all the static knowledge (such as technical vendor data, proprietary data types, and communication protocols) related to the significant energy consumers of the target infrastructure. Furthermore, this paper describes the overall methodology and how the common semantics offered by the ontology were utilized to improve the interoperability and energy management of complex infrastructures. Initially, a core facility ontology, which represents the generic facility model providing the general concepts behind the modelling, was defined. Results: In order to develop a full-blown model of the specific facility infrastructure, Malpensa and Fiumicino airports in Italy were taken as a test-bed platform in order to develop the airport ontology owing to the variety of the technical systems installed at the site. For the development of the airport ontology, the core facility ontology was first extended and then populated to reflect the actual state of the target airport facility. Conclusion: The developed ontology was tested in the environment of the two pilots, and the proposed solution proved to be a valuable link between separate ICT systems involving equipment from various vendors, both on syntax and semantic level, thus offering the facility managers the ability to retrieve high-level information regarding the performance of significant energy consumers.

Multi-Criteria optimisation using past, real time and predictive performance benchmarks

Abstract Performance based design, construction, commissioning and operation of buildings requires virtual testing and validation of project alternatives. In the case of environmental and energy management of buildings, whole Building Energy Simulation (BES) models can be used to determine indoor environmental conditions, building energy consumption, system performance, and associated CO 2 emissions, etc. BES is currently used at the design and commissioning phases of the Building Life Cycle (BLC) but not during the operational phase. This paper defines a methodology that incorporates predictive BES into building operation while acknowledging present technological limitations with respect to model accuracy and required resources. This predictive model also requires detailed definition and characterisation of inputs including: Historical data from buildings; Real time data such as measurements from meters and wired and wireless sensors underpinned by a Building Management System (BMS) and Future data such as short term weather forecast values and expected occupancy schedules. The paper concludes with a demonstration of the predictive BES model methodology using an existing building at University College Cork, Ireland.

Pervasive knowledge-based networking for maintenance inspection in smart buildings.

In modern Smart Buildings, systems monitor and regulate energy consumption, lighting levels, humidity and other environmental factors. However, with the number of data sources increasing, the resulting information glut can reduce decision making efficiency for facility management. A promising technique to mitigate this problem is the use of an integration platform allowing smart applications to process the volume of data and aid in reducing information overload for facility managers and others, such as maintenance engineers who utilise the data. This paper presents an approach for managing data volumes by only routing the data to users or smart applications that have expressly requested it. Experiences with a prototype implementation are also described.

Spatial representation in product modelling

An unambiguous definition of space is necessary before any attempt is made to develop product or process models for concurrent engineering in the AEC Industry. The ambiguity is the result of different and even conflicting approaches to its definition in the various phases of the building life cycle for different stakeholders, e.g. architects, engineers, and building services engineer etc. Some researchers consider space as an abstract property of things, while others consider it as a thing itself. Regardless of the definition, the space can be referred to as a collector of material objects and also as an object itself. This paper investigates the existing concepts and criteria of definition in various phases, compares the factual and ontological meaning, and specifies conceptual schemas for representation of space, geometry, and buildings.

Formulation of STEP compliant building product model data for CFD analysis

Computational modelling is a resource hungry discipline requiring extensive expertise and large amounts of time. The ISO-STEP (International Standards Organisation-Standard for the Exchange of Product Model data) is proposing an integrated product model for CFD applications. This paper presents a specification for a STEP compliant building product model for CFD analysis incorporating IBMs Open Source Visualisation.

Towards sustainable water networks: automated fault detection and diagnosis

The paper will present an overview of one of the Fault Detection and Diagnosis (FDD) systems developed within the Waternomics project. The FDD system has been developed basing on the hydraulic modeling of the water network, the real time values of flow and pressure obtained from installation of innovative ICT and commercial smart meters and the application of the Anomaly Detection with fast Incremental ClustEring (ADWICE) algorithm adapted for the drinking water network. The FDD system developed is useful when we have to consider more than one parameter at the same time to determine if an anomaly or fault is in place in a complex water network and the system is designed on purpose to cope with a larger features set. The new FDD system will be implemented in an Italian demo site, the Linate Airport Water network in Milan, where a large water distribution network is in place and where, due the many variables coming into play, it could be very difficult to detect anomalies with a low false alarm rate.

A systematic decision support framework and prioritization method for energy projects in industrial organisations

This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 through a TP agreement between the SFI Centre for Ireland’s Big Data and Analytics Research, ZuTec Inc. Ltd and Boston Scientific Corporation.This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 through a TP agreement between the SFI Centre for Ireland’s Big Data and Analytics Research, ZuTec Inc. Ltd and Boston Scientific Corporation.The 10th International Conference on Sustainable Energy and Environmental Protection – SEEP 2017 was organised on June 27th – 30th 2017 in Bled, Slovenia, by: • Faculty of Chemistry and Chemical Engineering, University of Maribor, Slovenia, • University of the West of Scotland, School of Engineering and The aim of SEEP2017 is to bring together the researches within the field of sustainable energy and environmental protection from all over the world. The contributed papers are grouped in 18 sessions in order to provide access to readers out of 300 contributions prepared by authors from 52 countries. We thank the distinguished plenary and keynote speakers and chairs who have kindly consented to participate at this conference. We are also grateful to all the authors for their papers and to all committee members. We believe that scientific results and professional debates shall not only be an incentive for development, but also for making new friendships and possible future scientific development projects.

Assessing capital investment on energy improvement projects from a global energy management perspective: a tri-generation case study

This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 through a TP agreement between the SFI Centre for Ireland’s Big Data and Analytics Research, ZuTec Inc. Ltd and Boston Scientific Corporation