Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Knowledge-based holistic energy management of public buildings

Howell, Shaun Kevin, Rezgui, Yacine ORCID: https://orcid.org/0000-0002-5711-8400 and Yuce, Baris ORCID: https://orcid.org/0000-0002-9937-1535 2014. Knowledge-based holistic energy management of public buildings. Presented at: 2014 International Conference on Computing in Civil and Building Engineering, Orlando, FL, USA, 23-25 June 2014. Published in: Issa, Raymond Issa ed. Computing in Civil and Building Engineering. Amercian Society of Civil Engineers, pp. 1667-1674. 10.1061/9780784413616.207

Full text not available from this repository.

Abstract

Much recent work has been conducted towards reducing the energy consumption of mixed mode buildings through rule-based automation. This paper presents a novel rule generation methodology through the simultaneous use of historical sensor data and theoretical models. The theoretical rule generation methodology will be explained, encompassing the development of an energy model to the inclusion of rules within the building specific ontology. This will include the development of use-case scenarios, their refinement through a sensitivity analysis and the generation of rules through an artificial neural network embedded evolutionary optimisation algorithm. Following a review of relevant literature, the methodology will be contextualised within the architecture of the KnoholEM project and applied to the first demonstration object, a mixed use public building in the Netherlands. An example of a rule generated will then be presented and the system will be considered within the existing solution space before comments regarding future developments.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Publisher: Amercian Society of Civil Engineers
Last Modified: 31 Oct 2022 09:35
URI: https://orca.cardiff.ac.uk/id/eprint/81633

Citation Data

Cited 7 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item