Building power consumption datasets: Survey, taxonomy and future directions
<p dir="ltr">In the last decade, extended efforts have been poured into energy efficiency. Several energy consumption datasets were henceforth published, with each dataset varying in properties, uses and limitations. For instance, building energy consumption patterns are sourced from...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , , |
| Published: |
2020
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1864513558853713920 |
|---|---|
| author | Yassine Himeur (14158821) |
| author2 | Abdullah Alsalemi (6951986) Faycal Bensaali (12427401) Abbes Amira (6952001) |
| author2_role | author author author |
| author_facet | Yassine Himeur (14158821) Abdullah Alsalemi (6951986) Faycal Bensaali (12427401) Abbes Amira (6952001) |
| author_role | author |
| dc.creator.none.fl_str_mv | Yassine Himeur (14158821) Abdullah Alsalemi (6951986) Faycal Bensaali (12427401) Abbes Amira (6952001) |
| dc.date.none.fl_str_mv | 2020-11-15T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.enbuild.2020.110404 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Building_power_consumption_datasets_Survey_taxonomy_and_future_directions/24249898 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Information and computing sciences Data management and data science Building power consumption datasets Energy efficiency Dataset collection Recommender systems Micro-moments Visualization |
| dc.title.none.fl_str_mv | Building power consumption datasets: Survey, taxonomy and future directions |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">In the last decade, extended efforts have been poured into energy efficiency. Several energy consumption datasets were henceforth published, with each dataset varying in properties, uses and limitations. For instance, building energy consumption patterns are sourced from several sources, including ambient conditions, user occupancy, weather conditions and consumer preferences. Thus, a proper understanding of the available datasets will result in a strong basis for improving energy efficiency. Starting from the necessity of a comprehensive review of existing databases, this work is proposed to survey, study and visualize the numerical and methodological nature of building energy consumption datasets. A total of thirty-one databases are examined and compared in terms of several features, such as the geographical location, period of collection, number of monitored households, sampling rate of collected data, number of sub-metered appliances, extracted features and release date. Furthermore, data collection platforms and related modules for data transmission, data storage and privacy concerns used in different datasets are also analyzed and compared. Based on the analytical study, a novel dataset has been presented, namely Qatar university dataset, which is an annotated power consumption anomaly detection dataset. The latter will be very useful for testing and training anomaly detection algorithms, and hence reducing wasted energy. Moving forward, a set of recommendations is derived to improve datasets collection, such as the adoption of multi-modal data collection, smart Internet of things data collection, low-cost hardware platforms and privacy and security mechanisms. In addition, future directions to improve datasets exploitation and utilization are identified, including the use of novel machine learning solutions, innovative visualization tools and explainable mobile recommender systems. Accordingly, a novel visualization strategy based on using power consumption micro-moments has been presented along with an example of deploying machine learning algorithms to classify the micro-moment classes and identify anomalous power usage.</p><h2>Other Information</h2><p dir="ltr">Published in: Energy and Buildings<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.enbuild.2020.110404" target="_blank">https://dx.doi.org/10.1016/j.enbuild.2020.110404</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_0fc92930383f8f6f2b0a48ce26c93b02 |
| identifier_str_mv | 10.1016/j.enbuild.2020.110404 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24249898 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Building power consumption datasets: Survey, taxonomy and future directionsYassine Himeur (14158821)Abdullah Alsalemi (6951986)Faycal Bensaali (12427401)Abbes Amira (6952001)EngineeringElectrical engineeringInformation and computing sciencesData management and data scienceBuilding power consumption datasetsEnergy efficiencyDataset collectionRecommender systemsMicro-momentsVisualization<p dir="ltr">In the last decade, extended efforts have been poured into energy efficiency. Several energy consumption datasets were henceforth published, with each dataset varying in properties, uses and limitations. For instance, building energy consumption patterns are sourced from several sources, including ambient conditions, user occupancy, weather conditions and consumer preferences. Thus, a proper understanding of the available datasets will result in a strong basis for improving energy efficiency. Starting from the necessity of a comprehensive review of existing databases, this work is proposed to survey, study and visualize the numerical and methodological nature of building energy consumption datasets. A total of thirty-one databases are examined and compared in terms of several features, such as the geographical location, period of collection, number of monitored households, sampling rate of collected data, number of sub-metered appliances, extracted features and release date. Furthermore, data collection platforms and related modules for data transmission, data storage and privacy concerns used in different datasets are also analyzed and compared. Based on the analytical study, a novel dataset has been presented, namely Qatar university dataset, which is an annotated power consumption anomaly detection dataset. The latter will be very useful for testing and training anomaly detection algorithms, and hence reducing wasted energy. Moving forward, a set of recommendations is derived to improve datasets collection, such as the adoption of multi-modal data collection, smart Internet of things data collection, low-cost hardware platforms and privacy and security mechanisms. In addition, future directions to improve datasets exploitation and utilization are identified, including the use of novel machine learning solutions, innovative visualization tools and explainable mobile recommender systems. Accordingly, a novel visualization strategy based on using power consumption micro-moments has been presented along with an example of deploying machine learning algorithms to classify the micro-moment classes and identify anomalous power usage.</p><h2>Other Information</h2><p dir="ltr">Published in: Energy and Buildings<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.enbuild.2020.110404" target="_blank">https://dx.doi.org/10.1016/j.enbuild.2020.110404</a></p>2020-11-15T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.enbuild.2020.110404https://figshare.com/articles/journal_contribution/Building_power_consumption_datasets_Survey_taxonomy_and_future_directions/24249898CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/242498982020-11-15T00:00:00Z |
| spellingShingle | Building power consumption datasets: Survey, taxonomy and future directions Yassine Himeur (14158821) Engineering Electrical engineering Information and computing sciences Data management and data science Building power consumption datasets Energy efficiency Dataset collection Recommender systems Micro-moments Visualization |
| status_str | publishedVersion |
| title | Building power consumption datasets: Survey, taxonomy and future directions |
| title_full | Building power consumption datasets: Survey, taxonomy and future directions |
| title_fullStr | Building power consumption datasets: Survey, taxonomy and future directions |
| title_full_unstemmed | Building power consumption datasets: Survey, taxonomy and future directions |
| title_short | Building power consumption datasets: Survey, taxonomy and future directions |
| title_sort | Building power consumption datasets: Survey, taxonomy and future directions |
| topic | Engineering Electrical engineering Information and computing sciences Data management and data science Building power consumption datasets Energy efficiency Dataset collection Recommender systems Micro-moments Visualization |