Rule-based scheduling of air conditioning using occupancy forecasting

Rule-based scheduling of air conditioning using occupancy forecasting

Heating, ventilation and air conditioning systems represent considerable potential for energy savings, which can be realized through intelligent occupancy-centered control strategies. In this work, both supervised and unsupervised algorithms to forecast occupancy are proposed with the highest accura...

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Bibliographic Details
Main Authors: Marina Dorokhova, Christophe Ballif, Nicolas Wyrsch
Format: Article
Language:English
Published: Elsevier 2020-11-01
Series:Energy and AI
Subjects:
Automation
Energy savings
HVAC control
Occupancy forecasting
Thermal comfort
Online Access:http://www.sciencedirect.com/science/article/pii/S2666546820300227
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spelling doaj-55d2d489286b4747883a0b718601eb712020-12-13T04:19:52ZengElsevierEnergy and AI2666-54682020-11-012100022Rule-based scheduling of air conditioning using occupancy forecastingMarina Dorokhova0Christophe Ballif1Nicolas Wyrsch2Corresponding author.; Photovoltaics and Thin-Film Electronics Laboratory (PV-Lab) Ecole Polytechnique Fédérale de Lausanne (EPFL), Institute of Microengineering (IMT) Rue de la Maladière 71b, Neuchâtel 2000, SwitzerlandPhotovoltaics and Thin-Film Electronics Laboratory (PV-Lab) Ecole Polytechnique Fédérale de Lausanne (EPFL), Institute of Microengineering (IMT) Rue de la Maladière 71b, Neuchâtel 2000, SwitzerlandPhotovoltaics and Thin-Film Electronics Laboratory (PV-Lab) Ecole Polytechnique Fédérale de Lausanne (EPFL), Institute of Microengineering (IMT) Rue de la Maladière 71b, Neuchâtel 2000, SwitzerlandHeating, ventilation and air conditioning systems represent considerable potential for energy savings, which can be realized through intelligent occupancy-centered control strategies. In this work, both supervised and unsupervised algorithms to forecast occupancy are proposed with the highest accuracies of 98.3% and 97.6%, respectively. Building on their output, a rule-based air conditioning scheduling technique is developed. As an example, a potential of 15.4% of energy savings is calculated using a dataset collected in a mid-size (4000 m2) building in Portugal.http://www.sciencedirect.com/science/article/pii/S2666546820300227AutomationEnergy savingsHVAC controlOccupancy forecastingThermal comfort
collection DOAJ
language English
format Article
sources DOAJ
author Marina Dorokhova
Christophe Ballif
Nicolas Wyrsch
spellingShingle Marina Dorokhova
Christophe Ballif
Nicolas Wyrsch
Rule-based scheduling of air conditioning using occupancy forecasting
Energy and AI
Automation
Energy savings
HVAC control
Occupancy forecasting
Thermal comfort
author_facet Marina Dorokhova
Christophe Ballif
Nicolas Wyrsch
author_sort Marina Dorokhova
title Rule-based scheduling of air conditioning using occupancy forecasting
title_short Rule-based scheduling of air conditioning using occupancy forecasting
title_full Rule-based scheduling of air conditioning using occupancy forecasting
title_fullStr Rule-based scheduling of air conditioning using occupancy forecasting
title_full_unstemmed Rule-based scheduling of air conditioning using occupancy forecasting
title_sort rule-based scheduling of air conditioning using occupancy forecasting
publisher Elsevier
series Energy and AI
issn 2666-5468
publishDate 2020-11-01
description Heating, ventilation and air conditioning systems represent considerable potential for energy savings, which can be realized through intelligent occupancy-centered control strategies. In this work, both supervised and unsupervised algorithms to forecast occupancy are proposed with the highest accuracies of 98.3% and 97.6%, respectively. Building on their output, a rule-based air conditioning scheduling technique is developed. As an example, a potential of 15.4% of energy savings is calculated using a dataset collected in a mid-size (4000 m2) building in Portugal.
topic Automation
Energy savings
HVAC control
Occupancy forecasting
Thermal comfort
url http://www.sciencedirect.com/science/article/pii/S2666546820300227
work_keys_str_mv AT marinadorokhova rulebasedschedulingofairconditioningusingoccupancyforecasting
AT christopheballif rulebasedschedulingofairconditioningusingoccupancyforecasting
AT nicolaswyrsch rulebasedschedulingofairconditioningusingoccupancyforecasting
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