A Data-Driven Methodology for Heating Optimization in Smart Buildings

Victoria Moreno, José Antonio Ferrer, José Alberto Díaz, Domingo Bravo, Victor Chang

Abstract

In the paradigm of Internet of Things new applications that leverage ubiquitous connectivity enable - together with Big Data Analytics - the emergence of Smart City initiatives. This paper proposes to build a closed loop data modeling methodology in order to optimize energy consumption in a fundamental smart city scenario: smart buildings. This methodology is based on the fusion of information about relevant parameters affecting energy consumption in buildings, and the application of recommended big data techniques in order to improve knowledge acquisition for better decision making and ensure energy efficiency. Experiments carried out in different buildings demonstrate the suitability of the proposed methodology.

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Paper Citation


in Harvard Style

Moreno V., Ferrer J., Díaz J., Bravo D. and Chang V. (2017). A Data-Driven Methodology for Heating Optimization in Smart Buildings . In Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-245-5, pages 19-29. DOI: 10.5220/0006231200190029


in Bibtex Style

@conference{iotbds17,
author={Victoria Moreno and José Antonio Ferrer and José Alberto Díaz and Domingo Bravo and Victor Chang},
title={A Data-Driven Methodology for Heating Optimization in Smart Buildings},
booktitle={Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2017},
pages={19-29},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006231200190029},
isbn={978-989-758-245-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - A Data-Driven Methodology for Heating Optimization in Smart Buildings
SN - 978-989-758-245-5
AU - Moreno V.
AU - Ferrer J.
AU - Díaz J.
AU - Bravo D.
AU - Chang V.
PY - 2017
SP - 19
EP - 29
DO - 10.5220/0006231200190029