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dc.contributor.authorMian Qaisar, Saeed
dc.contributor.authorLópez, Alberto
dc.contributor.authorKitanneh, Omar
dc.contributor.authorFerrero, Francisco
dc.date.accessioned2024-09-08T08:35:36Z
dc.date.available2024-09-08T08:35:36Z
dc.date.issued2024-11-15
dc.identifier.doi10.1109/I2MTC60896.2024.10560988en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/1781
dc.descriptionThis work is financially supportrd by the Effat University under the grant number (UC#9/3June2024/7.1-22(4)5)en_US
dc.description.abstractIn recent days the interest in the usage of smart meters is raising. It is evident from the widespread use of smart meters in contemporary society. The stakeholders in the smart grid must profit from the gathering and processing of fine-grained metering data. Time invariance characterizes the classical data sampling method. As a result, a significant volume of unnecessary data is gathered, sent, and analyzed. A method of adaptive delta-driven sampling (ADDS) of the smart meter data is proposed. It compensates the aforementioned shortfall and can lead towards a significant real-time compression without losing pertinent information. Subsequently, the compressed form of data can be efficiently processed, analyzed, stored and transmitted. It promises a significant transmission and computational effectiveness with a diminished latency. It is shown that the devised form of compressed data can be effectively reconstructed using a low complexity reconstruction algorithm. The reconstruction error is measured in terms of the root mean square error (RMSE) and the mean absolute error (MAE). The applicability is tested using the power consumption patterns of coffee machines, computer stations, fridges and freezers. The proposed solution attains an overall compression gain of 1.84-times, 2.49-times, 7.55-times respectively for the coffee machine, computer stations, and fridges and freezers. Moreover, the obtained values of RMSE and MAE confirm an appropriate reconstruction using the devised method.en_US
dc.description.sponsorshipEffat Universityen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectSmart Metersen_US
dc.subjectSignal Processingen_US
dc.subjectSmart Meter Dataen_US
dc.subjectReconstruction Erroren_US
dc.subjectRoot Mean Square Erroren_US
dc.subjectMean Square Erroren_US
dc.titleAppliances Load Pattern Reconstruction from Adaptive Delta-Driven Sampled Smart Meter Dataen_US
dc.source.journalInstrumentation and Measurement for a Sustainable Futureen_US
dc.contributor.researcherCollege collaborationen_US
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labArtificial Intelligence & Cyber Security Laben_US
dc.subject.KSAEnergy and Industrial Leadershipen_US
dc.contributor.ugstudent0en_US
dc.contributor.alumnae0en_US
dc.title.projectAdaptive Delta-Driven Sampling and Classification of the Smart Meter Dataen_US
dc.source.indexScopusen_US
dc.source.indexWoSen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.contributor.pgstudent0en_US
dc.contributor.firstauthorMian Qaisar, Saeed
dc.IR.KSAENERGYen_US
dc.IR.KSAICTen_US
dc.IR.KSAMTHen_US
dc.SDGs.KSASDG 8en_US
dc.SDGs.KSASDG 9en_US
dc.SDGs.KSASDG 11en_US
dc.IAW.KSANAen_US


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