Non intrusive load monitoring for demand side management

Abstract In the context of a pilot project, the Lugaggia Innovation Community (LIC), we address the problem of non-intrusive load monitoring for the purpose of demand side management on low voltage grids in presence of distributed power generation (photovoltaic). From the power readings of smart met...

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Main Authors: Matteo Salani, Marco Derboni, Davide Rivola, Vasco Medici, Lorenzo Nespoli, Federico Rosato, Andrea E. Rizzoli
Format: Article
Language:English
Published: SpringerOpen 2020-10-01
Series:Energy Informatics
Subjects:
Online Access:http://link.springer.com/article/10.1186/s42162-020-00128-2
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author Matteo Salani
Marco Derboni
Davide Rivola
Vasco Medici
Lorenzo Nespoli
Federico Rosato
Andrea E. Rizzoli
author_facet Matteo Salani
Marco Derboni
Davide Rivola
Vasco Medici
Lorenzo Nespoli
Federico Rosato
Andrea E. Rizzoli
author_sort Matteo Salani
collection DOAJ
description Abstract In the context of a pilot project, the Lugaggia Innovation Community (LIC), we address the problem of non-intrusive load monitoring for the purpose of demand side management on low voltage grids in presence of distributed power generation (photovoltaic). From the power readings of smart meters, we estimate the photovoltaic production and detect the activation of major loads (heatpumps and domestic water heaters). Experiments, conducted with real data and in silico, show that exploiting meter readings only, we can estimate PV production with MAPE ranging from 4.6% (best case) to 41.9% (worst case). Even with non negligible photovoltaic production estimation errors, the proposed method is capable of detecting the activation of heatpumps and domestic water heaters.
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spelling doaj.art-dfa3bd9a77ea490d86ad9e28170df8f72022-12-21T23:10:53ZengSpringerOpenEnergy Informatics2520-89422020-10-013S111210.1186/s42162-020-00128-2Non intrusive load monitoring for demand side managementMatteo Salani0Marco Derboni1Davide Rivola2Vasco Medici3Lorenzo Nespoli4Federico Rosato5Andrea E. Rizzoli6Dalle Molle Institute for Artificial Intelligence, USI-SUPSIDalle Molle Institute for Artificial Intelligence, USI-SUPSIInstitute for Applied Sustainability to the Built Environment, SUPSIInstitute for Applied Sustainability to the Built Environment, SUPSIInstitute for Applied Sustainability to the Built Environment, SUPSIInstitute for Applied Sustainability to the Built Environment, SUPSIDalle Molle Institute for Artificial Intelligence, USI-SUPSIAbstract In the context of a pilot project, the Lugaggia Innovation Community (LIC), we address the problem of non-intrusive load monitoring for the purpose of demand side management on low voltage grids in presence of distributed power generation (photovoltaic). From the power readings of smart meters, we estimate the photovoltaic production and detect the activation of major loads (heatpumps and domestic water heaters). Experiments, conducted with real data and in silico, show that exploiting meter readings only, we can estimate PV production with MAPE ranging from 4.6% (best case) to 41.9% (worst case). Even with non negligible photovoltaic production estimation errors, the proposed method is capable of detecting the activation of heatpumps and domestic water heaters.http://link.springer.com/article/10.1186/s42162-020-00128-2Smart GridLoad MonitoringDemand Side Management
spellingShingle Matteo Salani
Marco Derboni
Davide Rivola
Vasco Medici
Lorenzo Nespoli
Federico Rosato
Andrea E. Rizzoli
Non intrusive load monitoring for demand side management
Energy Informatics
Smart Grid
Load Monitoring
Demand Side Management
title Non intrusive load monitoring for demand side management
title_full Non intrusive load monitoring for demand side management
title_fullStr Non intrusive load monitoring for demand side management
title_full_unstemmed Non intrusive load monitoring for demand side management
title_short Non intrusive load monitoring for demand side management
title_sort non intrusive load monitoring for demand side management
topic Smart Grid
Load Monitoring
Demand Side Management
url http://link.springer.com/article/10.1186/s42162-020-00128-2
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