Optimization-Based Energy Disaggregation: A Constrained Multi-Objective Approach

Jeewon Park, Oladayo S. Ajani and Rammohan Mallipeddi ()
Additional contact information
Jeewon Park: Department of Artificial Intelligence, Kyungpook National University, Daegu 37224, Republic of Korea
Oladayo S. Ajani: Department of Artificial Intelligence, Kyungpook National University, Daegu 37224, Republic of Korea
Rammohan Mallipeddi: Department of Artificial Intelligence, Kyungpook National University, Daegu 37224, Republic of Korea

Mathematics, 2023, vol. 11, issue 3, 1-13

Abstract: Recently, optimization-based energy disaggregation (ED) algorithms have been gaining significance due to their capability to perform disaggregation with minimal information compared to the pattern-based ED algorithms, which demand large amounts of data for training. However, the performances of optimization-based ED algorithms depend on the problem formulation that includes an objective function(s) and/or constraints. In the literature, ED has been formulated as a constrained single-objective problem or an unconstrained multi-objective problem considering disaggregation error, sparsity of state switching, on/off switching, etc. In this work, the ED problem is formulated as a constrained multi-objective problem (CMOP), where the constraints related to the operational characteristics of the devices are included. In addition, the formulated CMOP is solved using a constrained multi-objective evolutionary algorithm (CMOEA). The performance of the proposed formulation is compared with those of three high-performing ED formulations in the literature based on the appliance-level and overall indicators. The results show that the proposed formulation improves both appliance-level and overall ED results.

Keywords: energy disaggregation; non-intrusive load monitoring; optimization-based energy disaggregation; constrained multi-objective optimization; evolutionary algorithms (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/3/563/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/3/563/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:3:p:563-:d:1042981

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().