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A modular optimisation model for reducing energy consumption in large scale building facilities

Petri, Ioan, Li, Haijiang, Rezgui, Yacine, Chunfeng, Yang, Yuce, Baris and Jayan, Bejay 2014. A modular optimisation model for reducing energy consumption in large scale building facilities. Renewable and Sustainable Energy Reviews 38 , pp. 990-1002. 10.1016/j.rser.2014.07.044

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Abstract

With the pressing regulatory requirement to increase energy efficiency in our built environment, significant researching efforts have been recently directed towards energy optimisation with the overall objective of reducing energy consumption. Energy simulation and optimisation identify a class of applications that demand high performance processing power in order to be realised within a feasible time-frame. The problem becomes increasingly complex when undertaking such energy simulation and optimisation in large scale buildings such as sport facilities where the generation of optimal set points can be timing inefficient. In this paper we present how a modular based optimisation system can be efficiently used for running energy simulation and optimisation in order to fulfil a number of energy related objectives. The solution can address the variability in building dynamics and provide support for building managers in implementing energy efficient optimisation plans. We present the optimisation system that has been implemented based on energy saving specifications from EU FP7 project – SportE2 (Energy Efficiency for Sport Facilities) and evaluate the efficiency of the system over a number of relevant use-case scenarios.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Engineering
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General)
Uncontrolled Keywords: Energy optimisation; Building simulation; Genetic algorithm; Artificial neural network; High performance computing.
Publisher: Elsevier
ISSN: 1364-0321
Last Modified: 26 Oct 2017 12:51
URI: http://orca-mwe.cf.ac.uk/id/eprint/62551

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