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Energy network modelling approaches for multi-scale building performance optimization

Energy network modelling approaches for multi-scale building performance optimization
Energy network modelling approaches for multi-scale building performance optimization

Energy dynamics in buildings can be described by means of different modelling approaches, depending of the specific purpose of the analysis, ranging from design phase simulation to energy management, optimal control, fault detection and diagnosis, etc. Network modelling formalism can help addressing energy related issues by simplifying physical representation. Further, the integrated use of robust computational techniques such as state-space models, transfer functions and time series models is crucial for the introduction of smart building technologies, conceived within the Internet of Things (IoT) paradigm. This technological paradigm can become a key enabler for the development of innovative and cost-effective solutions in building energy management and automation systems, aimed at high energy efficiency, low cost, flexibility and optimal interaction with infrastructures. However, the problem of modelling integration should be necessarily addressed to ensure the consistency of the proposed solutions. The research aims to present an analysis of the motivations to pursue a research in this direction, highlighting relevant features, opportunities and limitations.

automation, energy efficiency, energy modelling, IoT, network models
Institute of Electrical and Electronics Engineers Inc.
Tronchin, Lamberto
550312d9-21be-41d8-8947-a0d6fb5c52a8
Manfren, Massimiliano
f2b8c02d-cb78-411d-aed1-c4d056365392
Tronchin, Lamberto
550312d9-21be-41d8-8947-a0d6fb5c52a8
Manfren, Massimiliano
f2b8c02d-cb78-411d-aed1-c4d056365392

Tronchin, Lamberto and Manfren, Massimiliano (2018) Energy network modelling approaches for multi-scale building performance optimization. In Proceedings - 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018. Institute of Electrical and Electronics Engineers Inc.. (doi:10.1109/EEEIC.2018.8494369).

Record type: Conference or Workshop Item (Paper)

Abstract

Energy dynamics in buildings can be described by means of different modelling approaches, depending of the specific purpose of the analysis, ranging from design phase simulation to energy management, optimal control, fault detection and diagnosis, etc. Network modelling formalism can help addressing energy related issues by simplifying physical representation. Further, the integrated use of robust computational techniques such as state-space models, transfer functions and time series models is crucial for the introduction of smart building technologies, conceived within the Internet of Things (IoT) paradigm. This technological paradigm can become a key enabler for the development of innovative and cost-effective solutions in building energy management and automation systems, aimed at high energy efficiency, low cost, flexibility and optimal interaction with infrastructures. However, the problem of modelling integration should be necessarily addressed to ensure the consistency of the proposed solutions. The research aims to present an analysis of the motivations to pursue a research in this direction, highlighting relevant features, opportunities and limitations.

Full text not available from this repository.

More information

Published date: 16 October 2018
Venue - Dates: 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018, Palermo, Italy, 2018-06-12 - 2018-06-15
Keywords: automation, energy efficiency, energy modelling, IoT, network models

Identifiers

Local EPrints ID: 426497
URI: http://eprints.soton.ac.uk/id/eprint/426497
PURE UUID: caebebb8-49ff-44da-9519-dfc7568d3b66
ORCID for Massimiliano Manfren: ORCID iD orcid.org/0000-0003-1438-970X

Catalogue record

Date deposited: 29 Nov 2018 17:30
Last modified: 20 Jul 2019 00:27

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