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

被引:40
|
作者
Petri, Ioan [1 ]
Li, Haijiang [1 ]
Rezgui, Yacine [1 ]
Yang Chunfeng [2 ]
Yuce, Baris [1 ]
Jayan, Bejay [1 ]
机构
[1] Cardiff Univ, Sch Engn, BRE Inst Sustainable Engn, Cardiff CF10 3AX, S Glam, Wales
[2] Dalian Univ Technol, State Key Lab Struct Anal Ind Equipment, Dept Engn Mech, Fac Vehicle Engn & Mech, Dalian, Peoples R China
来源
关键词
Energy optimisation; Building simulation; Genetic algorithm; Artificial neural network; High performance computing; SIMULATION;
D O I
10.1016/j.rser.2014.07.044
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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 - SportE(2) (Energy Efficiency for Sport Facilities) and evaluate the efficiency of the system over a number of relevant use-case scenarios. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:990 / 1002
页数:13
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