Achieving Domestic Energy Efficiency Using Micro-Moments and Intelligent Recommendations
<p dir="ltr">Excessive domestic energy usage is an impediment towards energy efficiency. Developing countries are expected to witness an unprecedented rise in domestic electricity in the forthcoming decades. A large amount of research has been directed towards behavioral change for e...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2020
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| _version_ | 1864513561124929536 |
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| author | Abdullah Alsalemi (6951986) |
| author2 | Yassine Himeur (14158821) Fayal Bensaali (16855422) Abbes Amira (6952001) Christos Sardianos (8297297) Iraklis Varlamis (9288743) George Dimitrakopoulos (16855419) |
| author2_role | author author author author author author |
| author_facet | Abdullah Alsalemi (6951986) Yassine Himeur (14158821) Fayal Bensaali (16855422) Abbes Amira (6952001) Christos Sardianos (8297297) Iraklis Varlamis (9288743) George Dimitrakopoulos (16855419) |
| author_role | author |
| dc.creator.none.fl_str_mv | Abdullah Alsalemi (6951986) Yassine Himeur (14158821) Fayal Bensaali (16855422) Abbes Amira (6952001) Christos Sardianos (8297297) Iraklis Varlamis (9288743) George Dimitrakopoulos (16855419) |
| dc.date.none.fl_str_mv | 2020-01-27T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2020.2966640 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Achieving_Domestic_Energy_Efficiency_Using_Micro-Moments_and_Intelligent_Recommendations/23994834 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Data management and data science Distributed computing and systems software Energy consumption Temperature sensors Databases Humidity Energy efficiency Data visualization Classification Domestic energy usage Micro-moment Mobile application Recommender system |
| dc.title.none.fl_str_mv | Achieving Domestic Energy Efficiency Using Micro-Moments and Intelligent Recommendations |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Excessive domestic energy usage is an impediment towards energy efficiency. Developing countries are expected to witness an unprecedented rise in domestic electricity in the forthcoming decades. A large amount of research has been directed towards behavioral change for energy efficiency. Thus, it is prudent to develop an intelligent system that combines the proper use of technology with behavior change research in order to sustainably transform end-user behavior at a large scale. This paper presents an overview of our AI-based energy efficiency framework for domestic applications and explains how micro-moments can provide an accurate understanding of user behavior and lead to more effective recommendations. Micro-moments are short-term events at which an energy-saving recommendation is presented to the consumer. They are detected using a variety of sensing modules placed at prominent locations in the household. A supervised machine learning classifier is then used to analyze the acquired micro-moments, identify abnormalities, and formulate a list of energy-saving recommendations. Each recommendation is presented through the end-user mobile application. The results so far include a mobile application in the front-end and a set of sensing modules in the backend that comprise, an ensemble bagging-trees micro-moment classifier (achieving up to 99.64% accuracy and 98.8% F-score), and a recommendation engine.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2020.2966640" target="_blank">https://dx.doi.org/10.1109/access.2020.2966640</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_b9640a6279313395e5e9e2c1a61c4304 |
| identifier_str_mv | 10.1109/access.2020.2966640 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/23994834 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Achieving Domestic Energy Efficiency Using Micro-Moments and Intelligent RecommendationsAbdullah Alsalemi (6951986)Yassine Himeur (14158821)Fayal Bensaali (16855422)Abbes Amira (6952001)Christos Sardianos (8297297)Iraklis Varlamis (9288743)George Dimitrakopoulos (16855419)EngineeringElectrical engineeringElectronics, sensors and digital hardwareInformation and computing sciencesArtificial intelligenceData management and data scienceDistributed computing and systems softwareEnergy consumptionTemperature sensorsDatabasesHumidityEnergy efficiencyData visualizationClassificationDomestic energy usageMicro-momentMobile applicationRecommender system<p dir="ltr">Excessive domestic energy usage is an impediment towards energy efficiency. Developing countries are expected to witness an unprecedented rise in domestic electricity in the forthcoming decades. A large amount of research has been directed towards behavioral change for energy efficiency. Thus, it is prudent to develop an intelligent system that combines the proper use of technology with behavior change research in order to sustainably transform end-user behavior at a large scale. This paper presents an overview of our AI-based energy efficiency framework for domestic applications and explains how micro-moments can provide an accurate understanding of user behavior and lead to more effective recommendations. Micro-moments are short-term events at which an energy-saving recommendation is presented to the consumer. They are detected using a variety of sensing modules placed at prominent locations in the household. A supervised machine learning classifier is then used to analyze the acquired micro-moments, identify abnormalities, and formulate a list of energy-saving recommendations. Each recommendation is presented through the end-user mobile application. The results so far include a mobile application in the front-end and a set of sensing modules in the backend that comprise, an ensemble bagging-trees micro-moment classifier (achieving up to 99.64% accuracy and 98.8% F-score), and a recommendation engine.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2020.2966640" target="_blank">https://dx.doi.org/10.1109/access.2020.2966640</a></p>2020-01-27T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2020.2966640https://figshare.com/articles/journal_contribution/Achieving_Domestic_Energy_Efficiency_Using_Micro-Moments_and_Intelligent_Recommendations/23994834CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/239948342020-01-27T00:00:00Z |
| spellingShingle | Achieving Domestic Energy Efficiency Using Micro-Moments and Intelligent Recommendations Abdullah Alsalemi (6951986) Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Data management and data science Distributed computing and systems software Energy consumption Temperature sensors Databases Humidity Energy efficiency Data visualization Classification Domestic energy usage Micro-moment Mobile application Recommender system |
| status_str | publishedVersion |
| title | Achieving Domestic Energy Efficiency Using Micro-Moments and Intelligent Recommendations |
| title_full | Achieving Domestic Energy Efficiency Using Micro-Moments and Intelligent Recommendations |
| title_fullStr | Achieving Domestic Energy Efficiency Using Micro-Moments and Intelligent Recommendations |
| title_full_unstemmed | Achieving Domestic Energy Efficiency Using Micro-Moments and Intelligent Recommendations |
| title_short | Achieving Domestic Energy Efficiency Using Micro-Moments and Intelligent Recommendations |
| title_sort | Achieving Domestic Energy Efficiency Using Micro-Moments and Intelligent Recommendations |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Data management and data science Distributed computing and systems software Energy consumption Temperature sensors Databases Humidity Energy efficiency Data visualization Classification Domestic energy usage Micro-moment Mobile application Recommender system |