A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control

Jonathan Reynolds, Yacine Rezgui, Alan Kwan and Solène Piriou

Energy, 2018, vol. 151, issue C, 729-739

Abstract: Buildings account for a substantial proportion of global energy consumption and global greenhouse gas emissions. Given the growth in smart devices and sensors there is an opportunity to develop a new generation of smarter, more context aware, building controllers. Therefore, in this work, surrogate, zone-level artificial neural networks that take weather, occupancy and indoor temperature as inputs, have been created. These are used as an evaluation engine by a genetic algorithm with the aim of minimising energy consumption. Bespoke 24-h, heating set point schedules are generated for each zone in a small office building in Cardiff, UK. The optimisation strategy can be deployed in two modes, day ahead optimisation or as model predictive control which re-optimises every hour. Over a February test week, the optimisation is shown to reduce energy consumption by around 25% compared to a baseline heating strategy. When a time of use tariff is introduced, the optimisation is altered to minimise cost rather than energy consumption. The optimisation strategy successfully shifts load to cheaper price periods and reduces energy cost by around 27% compared to the baseline strategy.

Keywords: Building energy management; Artificial neural network; Genetic algorithm; Model predictive control; HVAC control; Heating set point scheduler (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (45)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054421830522X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:151:y:2018:i:c:p:729-739

DOI: 10.1016/j.energy.2018.03.113

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().