A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural Networks
Nowadays, measurement systems strongly rely on the Internet of Things paradigm, and typically involve miniaturized devices on purpose. In these devices, the computational resources and signal acquisition rates are limited in order to preserve battery life. In addition, the amount of streamed data is...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/7/1371 |
| _version_ | 1818039114527670272 |
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| author | Marco Fagiani Roberto Bonfigli Emanuele Principi Stefano Squartini Luigi Mandolini |
| author_facet | Marco Fagiani Roberto Bonfigli Emanuele Principi Stefano Squartini Luigi Mandolini |
| author_sort | Marco Fagiani |
| collection | DOAJ |
| description | Nowadays, measurement systems strongly rely on the Internet of Things paradigm, and typically involve miniaturized devices on purpose. In these devices, the computational resources and signal acquisition rates are limited in order to preserve battery life. In addition, the amount of streamed data is affected by the network capacity strictly related to the transmission protocol constraints and the environmental conditions. All those limitations are in contrast with the need of exploiting all possible signal details for the task under study. In the specific application of interest, i.e., Non-Intrusive Load Monitoring (NILM), they could lead to low performance in the energy disaggregation process. To overcome these issues, an ad hoc data reduction policy needs to be adopted, in order to reduce the acquisition and elaboration burden of the device, and, at the same time, to ensure compliance with network bandwidth limits while maintaining a reliable signal representation. Moved by these motivations, an extended evaluation study concerning the application of data reduction strategy to the aggregate signal is presented in this work. In particular, a non-uniform subsampling (NUS) scheme is defined together with a uniform subsampling (US) strategy and compared, in terms of disaggregation performance, with the use of data at original sampling (OS) rate. A Deep Learning based technique is used for disaggregation, having the aggregate active power signal sampled according to diverse sampling schema mentioned above as input. The approaches are tested on the UK-DALE and REDD datasets, and the combination of US+NUS configurations allows for achieving a good performance in terms of <inline-formula> <math display="inline"> <semantics> <msub> <mi>F</mi> <mn>1</mn> </msub> </semantics> </math> </inline-formula>-score, even superior than the one obtained with the OS rate, and a remarkable data reduction at the same time. |
| first_indexed | 2024-12-10T07:53:29Z |
| format | Article |
| id | doaj.art-3161053353fc40e19e239cad9564e37c |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-12-10T07:53:29Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-3161053353fc40e19e239cad9564e37c2022-12-22T01:56:57ZengMDPI AGEnergies1996-10732019-04-01127137110.3390/en12071371en12071371A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural NetworksMarco Fagiani0Roberto Bonfigli1Emanuele Principi2Stefano Squartini3Luigi Mandolini4Department of Information Engineering (DII), Università Politecnica delle Marche, Via Brecce Bianche 1, 60131 Ancona, ItalyResearch and Development Area, MAC Srl, Via XX Settembre 23, 62019 Recanati (MC), ItalyDepartment of Information Engineering (DII), Università Politecnica delle Marche, Via Brecce Bianche 1, 60131 Ancona, ItalyDepartment of Information Engineering (DII), Università Politecnica delle Marche, Via Brecce Bianche 1, 60131 Ancona, ItalyResearch and Development Area, MAC Srl, Via XX Settembre 23, 62019 Recanati (MC), ItalyNowadays, measurement systems strongly rely on the Internet of Things paradigm, and typically involve miniaturized devices on purpose. In these devices, the computational resources and signal acquisition rates are limited in order to preserve battery life. In addition, the amount of streamed data is affected by the network capacity strictly related to the transmission protocol constraints and the environmental conditions. All those limitations are in contrast with the need of exploiting all possible signal details for the task under study. In the specific application of interest, i.e., Non-Intrusive Load Monitoring (NILM), they could lead to low performance in the energy disaggregation process. To overcome these issues, an ad hoc data reduction policy needs to be adopted, in order to reduce the acquisition and elaboration burden of the device, and, at the same time, to ensure compliance with network bandwidth limits while maintaining a reliable signal representation. Moved by these motivations, an extended evaluation study concerning the application of data reduction strategy to the aggregate signal is presented in this work. In particular, a non-uniform subsampling (NUS) scheme is defined together with a uniform subsampling (US) strategy and compared, in terms of disaggregation performance, with the use of data at original sampling (OS) rate. A Deep Learning based technique is used for disaggregation, having the aggregate active power signal sampled according to diverse sampling schema mentioned above as input. The approaches are tested on the UK-DALE and REDD datasets, and the combination of US+NUS configurations allows for achieving a good performance in terms of <inline-formula> <math display="inline"> <semantics> <msub> <mi>F</mi> <mn>1</mn> </msub> </semantics> </math> </inline-formula>-score, even superior than the one obtained with the OS rate, and a remarkable data reduction at the same time.https://www.mdpi.com/1996-1073/12/7/1371non-intrusive load monitoringenergy disaggregationdeep learningneural networksnon-uniform samplingactive powercomputational energy management |
| spellingShingle | Marco Fagiani Roberto Bonfigli Emanuele Principi Stefano Squartini Luigi Mandolini A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural Networks Energies non-intrusive load monitoring energy disaggregation deep learning neural networks non-uniform sampling active power computational energy management |
| title | A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural Networks |
| title_full | A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural Networks |
| title_fullStr | A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural Networks |
| title_full_unstemmed | A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural Networks |
| title_short | A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural Networks |
| title_sort | non intrusive load monitoring algorithm based on non uniform sampling of power data and deep neural networks |
| topic | non-intrusive load monitoring energy disaggregation deep learning neural networks non-uniform sampling active power computational energy management |
| url | https://www.mdpi.com/1996-1073/12/7/1371 |
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