eLAMI—An Innovative Simulated Dataset of Electrical Loads for Advanced Smart Energy Applications
Smart Energy Applications are particularly impacting, especially due to energy resource scarcity and its high associated costs. Smart management of energy consumption derives both from the user lifestyle, in terms of efficient and responsible behaviors, and from automatic algorithms that control and...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9868005/ |
| _version_ | 1827888284591718400 |
|---|---|
| author | Luca Tari Giuseppe Berrettoni Carmine Bourelly Gianni Cerro Domenico Capriglione Luigi Ferrigno |
| author_facet | Luca Tari Giuseppe Berrettoni Carmine Bourelly Gianni Cerro Domenico Capriglione Luigi Ferrigno |
| author_sort | Luca Tari |
| collection | DOAJ |
| description | Smart Energy Applications are particularly impacting, especially due to energy resource scarcity and its high associated costs. Smart management of energy consumption derives both from the user lifestyle, in terms of efficient and responsible behaviors, and from automatic algorithms that control and counteract energy waste and inefficient management. Focusing the attention on the latter, the development of methodologies and well-working techniques to monitor and optimize consumption often requires an important effort in long measurement campaigns to get raw data to work with. Whenever this should be too much expensive or proper instrumentation is unavailable, public datasets could solve the problem. The current literature review on the dataset availability showed a large presence of information, especially related to electrical energy consumption. Nevertheless, several limitations affect them, from the low number of calculated electrical parameters (i.e. 4-5 in most cases) to short analysis periods, passing by the lack of detailed frequency domain information or poor consumption habit transitions analysis. Accordingly, this work aims to overcome current dataset limitations, by proposing a real-measurement based simulated dataset, extracting more than 400 discriminative electrical parameters on 36 different home appliances, discussing preliminary acquisition set-ups, simulation process, extracted electrical parameters and examples of applicability to smart energy applications. To provide a data quality index, a validation procedure has also been carried out, showing how simulated data match real acquisition with a reference measurement instrument. The produced dataset is available for downloading and analysis in public free access and its repository link is provided in the reference section. |
| first_indexed | 2024-03-12T20:31:36Z |
| format | Article |
| id | doaj.art-b3db9dab4e15472081df6082e7ed10c7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T20:31:36Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b3db9dab4e15472081df6082e7ed10c72023-08-01T23:00:48ZengIEEEIEEE Access2169-35362022-01-0110911779119110.1109/ACCESS.2022.32019609868005eLAMI—An Innovative Simulated Dataset of Electrical Loads for Advanced Smart Energy ApplicationsLuca Tari0https://orcid.org/0000-0002-4307-0362Giuseppe Berrettoni1https://orcid.org/0000-0002-5458-676XCarmine Bourelly2https://orcid.org/0000-0003-4904-4162Gianni Cerro3https://orcid.org/0000-0002-6843-7140Domenico Capriglione4https://orcid.org/0000-0001-8449-1406Luigi Ferrigno5https://orcid.org/0000-0002-1724-5720Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, Cassino, ItalyDepartment of Electrical and Information Engineering, University of Cassino and Southern Lazio, Cassino, ItalyDepartment of Electrical and Information Engineering, University of Cassino and Southern Lazio, Cassino, ItalyDepartment of Medicine and Health Sciences, University of Molise, Campobasso, ItalyDepartment of Electrical and Information Engineering, University of Cassino and Southern Lazio, Cassino, ItalyDepartment of Electrical and Information Engineering, University of Cassino and Southern Lazio, Cassino, ItalySmart Energy Applications are particularly impacting, especially due to energy resource scarcity and its high associated costs. Smart management of energy consumption derives both from the user lifestyle, in terms of efficient and responsible behaviors, and from automatic algorithms that control and counteract energy waste and inefficient management. Focusing the attention on the latter, the development of methodologies and well-working techniques to monitor and optimize consumption often requires an important effort in long measurement campaigns to get raw data to work with. Whenever this should be too much expensive or proper instrumentation is unavailable, public datasets could solve the problem. The current literature review on the dataset availability showed a large presence of information, especially related to electrical energy consumption. Nevertheless, several limitations affect them, from the low number of calculated electrical parameters (i.e. 4-5 in most cases) to short analysis periods, passing by the lack of detailed frequency domain information or poor consumption habit transitions analysis. Accordingly, this work aims to overcome current dataset limitations, by proposing a real-measurement based simulated dataset, extracting more than 400 discriminative electrical parameters on 36 different home appliances, discussing preliminary acquisition set-ups, simulation process, extracted electrical parameters and examples of applicability to smart energy applications. To provide a data quality index, a validation procedure has also been carried out, showing how simulated data match real acquisition with a reference measurement instrument. The produced dataset is available for downloading and analysis in public free access and its repository link is provided in the reference section.https://ieeexplore.ieee.org/document/9868005/Smart energydatasetenergy consumption simulatorsmart homeload profilingnon-intrusive load monitoring |
| spellingShingle | Luca Tari Giuseppe Berrettoni Carmine Bourelly Gianni Cerro Domenico Capriglione Luigi Ferrigno eLAMI—An Innovative Simulated Dataset of Electrical Loads for Advanced Smart Energy Applications IEEE Access Smart energy dataset energy consumption simulator smart home load profiling non-intrusive load monitoring |
| title | eLAMI—An Innovative Simulated Dataset of Electrical Loads for Advanced Smart Energy Applications |
| title_full | eLAMI—An Innovative Simulated Dataset of Electrical Loads for Advanced Smart Energy Applications |
| title_fullStr | eLAMI—An Innovative Simulated Dataset of Electrical Loads for Advanced Smart Energy Applications |
| title_full_unstemmed | eLAMI—An Innovative Simulated Dataset of Electrical Loads for Advanced Smart Energy Applications |
| title_short | eLAMI—An Innovative Simulated Dataset of Electrical Loads for Advanced Smart Energy Applications |
| title_sort | elami x2014 an innovative simulated dataset of electrical loads for advanced smart energy applications |
| topic | Smart energy dataset energy consumption simulator smart home load profiling non-intrusive load monitoring |
| url | https://ieeexplore.ieee.org/document/9868005/ |
| work_keys_str_mv | AT lucatari elamix2014aninnovativesimulateddatasetofelectricalloadsforadvancedsmartenergyapplications AT giuseppeberrettoni elamix2014aninnovativesimulateddatasetofelectricalloadsforadvancedsmartenergyapplications AT carminebourelly elamix2014aninnovativesimulateddatasetofelectricalloadsforadvancedsmartenergyapplications AT giannicerro elamix2014aninnovativesimulateddatasetofelectricalloadsforadvancedsmartenergyapplications AT domenicocapriglione elamix2014aninnovativesimulateddatasetofelectricalloadsforadvancedsmartenergyapplications AT luigiferrigno elamix2014aninnovativesimulateddatasetofelectricalloadsforadvancedsmartenergyapplications |