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A Practical Load Disaggregation Approach for Monitoring Industrial Users Demand with Limited Data Availability

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  • Sara Tavakoli

    (Iran Grid Management Company, Tehran 1996836111, Iran
    Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, Australia)

  • Kaveh Khalilpour

    (Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, Australia)

Abstract

The emergence of smart sensors has had a significant impact on the utility industry. In particular, it has made the planning and implementation of demand-side management (DSM) programmes easier. Nevertheless, for various reasons, some users may not implement smart meters for load monitoring. This paper addresses such cases, particularly large-scale industrial users, which, despite heavy electrical loads coming from many different processes, implement only simple energy measuring equipment for billing purposes. This necessitates the utilisation of novel methodologies for load disaggregation, often referred to as nonintrusive load monitoring (NILM). The availability of such tools can create multifold benefits for industrial park management, utility service providers, regulators, and policymakers. Here, we introduce an optimisation algorithm for nonintrusive load disaggregation that is low-cost, speedy, and acceptably accurate. As a case study, we used real network data of three industrial sectors: food processing, stonecutting, and glassmaking. For all cases, the optimisation framework developed a desegregated profile and estimated the load with an error of less than 5%. For non-workdays, given the higher uncertainty for the continuity of different processes, the estimation error was higher but still in an acceptable range of around 3.63–15.09% with an average of 8.10%.

Suggested Citation

  • Sara Tavakoli & Kaveh Khalilpour, 2021. "A Practical Load Disaggregation Approach for Monitoring Industrial Users Demand with Limited Data Availability," Energies, MDPI, vol. 14(16), pages 1-27, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4880-:d:611620
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    References listed on IDEAS

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    Cited by:

    1. Wang, Zhongrui & Xu, Yonghai & He, Sheng & Yuan, Jindou & Yang, Heng & Pan, Mingming, 2023. "A non-intrusive method of industrial load disaggregation based on load operating states and improved grey wolf algorithm," Applied Energy, Elsevier, vol. 351(C).

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