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Optimum Energy Management for Air Conditioners in IoT-Enabled Smart Home

This paper addresses the optimal pre-cooling problem for air conditioners (AC) used in Internet of Things (IoT)-enabled smart homes while ensuring that user-defined thermal comfort can be achieved. The proposed strategy utilises renewable energy generation periods and moves some of the air condition...

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Detalles Bibliográficos
Autores principales: Philip, Ashleigh, Islam, Shama Naz, Phillips, Nicholas, Anwar, Adnan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571678/
https://www.ncbi.nlm.nih.gov/pubmed/36236199
http://dx.doi.org/10.3390/s22197102
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author Philip, Ashleigh
Islam, Shama Naz
Phillips, Nicholas
Anwar, Adnan
author_facet Philip, Ashleigh
Islam, Shama Naz
Phillips, Nicholas
Anwar, Adnan
author_sort Philip, Ashleigh
collection PubMed
description This paper addresses the optimal pre-cooling problem for air conditioners (AC) used in Internet of Things (IoT)-enabled smart homes while ensuring that user-defined thermal comfort can be achieved. The proposed strategy utilises renewable energy generation periods and moves some of the air conditioning loads to these periods to reduce the electricity demand. In particular, we propose a multi-stage approach which maximises the utilisation of renewable energy at the first stage to satisfy air conditioning loads, and then schedules residual energy consumption of these loads to low price periods at the second stage. The proposed approach is investigated for the temperature and renewable generation data of NSW, Australia, over the period 2012–2013. It is shown that the approach developed can significantly reduce the energy consumption and cost associated with AC operation for nearly all days in summer when cooling is required. Specifically, the proposed approach was found to achieve a 24% cost saving in comparison to the no pre-cooling case for the highest average temperature day in January, 2013. The analysis also demonstrated that the proposed scheme performed better when the thermal insulation levels in the smart home are higher. However, the optimal pre-cooling scheme can still achieve reduced energy costs under lower thermal insulation conditions compared to the no pre-cooling case.
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spelling pubmed-95716782022-10-17 Optimum Energy Management for Air Conditioners in IoT-Enabled Smart Home Philip, Ashleigh Islam, Shama Naz Phillips, Nicholas Anwar, Adnan Sensors (Basel) Article This paper addresses the optimal pre-cooling problem for air conditioners (AC) used in Internet of Things (IoT)-enabled smart homes while ensuring that user-defined thermal comfort can be achieved. The proposed strategy utilises renewable energy generation periods and moves some of the air conditioning loads to these periods to reduce the electricity demand. In particular, we propose a multi-stage approach which maximises the utilisation of renewable energy at the first stage to satisfy air conditioning loads, and then schedules residual energy consumption of these loads to low price periods at the second stage. The proposed approach is investigated for the temperature and renewable generation data of NSW, Australia, over the period 2012–2013. It is shown that the approach developed can significantly reduce the energy consumption and cost associated with AC operation for nearly all days in summer when cooling is required. Specifically, the proposed approach was found to achieve a 24% cost saving in comparison to the no pre-cooling case for the highest average temperature day in January, 2013. The analysis also demonstrated that the proposed scheme performed better when the thermal insulation levels in the smart home are higher. However, the optimal pre-cooling scheme can still achieve reduced energy costs under lower thermal insulation conditions compared to the no pre-cooling case. MDPI 2022-09-20 /pmc/articles/PMC9571678/ /pubmed/36236199 http://dx.doi.org/10.3390/s22197102 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Philip, Ashleigh
Islam, Shama Naz
Phillips, Nicholas
Anwar, Adnan
Optimum Energy Management for Air Conditioners in IoT-Enabled Smart Home
title Optimum Energy Management for Air Conditioners in IoT-Enabled Smart Home
title_full Optimum Energy Management for Air Conditioners in IoT-Enabled Smart Home
title_fullStr Optimum Energy Management for Air Conditioners in IoT-Enabled Smart Home
title_full_unstemmed Optimum Energy Management for Air Conditioners in IoT-Enabled Smart Home
title_short Optimum Energy Management for Air Conditioners in IoT-Enabled Smart Home
title_sort optimum energy management for air conditioners in iot-enabled smart home
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571678/
https://www.ncbi.nlm.nih.gov/pubmed/36236199
http://dx.doi.org/10.3390/s22197102
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