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Data-Driven User-Aware HVAC Scheduling

DocUID: 2018-006 Full Text: PDF

Author: Daniel Petrov, Rakan Alseghayer, Daniel Mosse, Panos K. Chrysanthis

Abstract: HVAC (Heat, Ventilation, Air Conditioning) systems account for significant amount of energy spent in residential and commercial buildings. Improved wall and window insulation, energy efficient bulbs as well as building design that facilitates a more optimal usage of the thermally conditioned air within a building, are amongst some of the measures taken to address the high usage of energy for space conditioning. In this paper we address a main issue that affects the energy consumption for heating and cooling of buildings, namely the duty cycle of the furnaces / air-conditioners. We propose D-DUAL, a 3-fold scheduling mechanism that builds on multiple variable linear regression model. Our scheduler minimizes the duty cycle and does not impact users’ comfort. Our experimental evaluation shows that our proposed approach saves up to 49% energy, compared to commodity HVAC systems.

Keywords: IoT; HVAC; scheduling; smart home; energy savings; Internet of Things

Published In: THE 9th International Green and Sustainable Computing Conference

ISBN: 978-1-5386-7466-6/18

Pages: 1-8

Place Published: Pittsburgh, PA, U.S.A.

Year Published: 2018

Project: Others Subject Area: Sensor Databases, Smart Buildings, Energy Efficiency, IoT

Publication Type: Conference Paper

Sponsor: RDF

Citation:Text Latex BibTex XML Daniel Petrov, Rakan Alseghayer, Daniel Mosse, and Panos K. Chrysanthis. Data-Driven User-Aware HVAC Scheduling. THE 9th International Green and Sustainable Computing Conference. 1-8. 2018. Pittsburgh, PA, U.S.A..