Research of Non-Intrusive Load Decomposition Considering Rooftop PV Based on IDPC-SHMM

Xingqi Liu (), Xuan Liu, Angang Zheng, Jian Dou and Yina Du
Additional contact information
Xingqi Liu: China Electric Power Research Institute, Metrology Institute, Beijing 100192, China
Xuan Liu: China Electric Power Research Institute, Metrology Institute, Beijing 100192, China
Angang Zheng: China Electric Power Research Institute, Metrology Institute, Beijing 100192, China
Jian Dou: China Electric Power Research Institute, Metrology Institute, Beijing 100192, China
Yina Du: China Electric Power Research Institute, Metrology Institute, Beijing 100192, China

Energies, 2025, vol. 18, issue 18, 1-16

Abstract: Household electricity meters equipped with rooftop photovoltaic systems only display net load power data after coupling loads with photovoltaic power, which gives rise to the issue of unknown PV output and load demand. A non-invasive load decomposition algorithm based on Improved Density Peak Clustering (IDPC) and the Simplified Hidden Markov Model (SHMM) is proposed to decompose PV generation power and load consumption power from net load power data, providing data support for power demand-side management. First, the Improved Density Peak Clustering algorithm is used to adaptively obtain load power templates. Then, historical power data from PV proxy sites are classified based on weather types, while radiation proxies are used to estimate the historical PV power of the target users. These estimated PV power data are combined with historical load information to derive the parameters of the SHMM under different PV output conditions, thereby constructing the load decomposition objective function. Finally, the net load power data are used to achieve non-intrusive load decomposition and photovoltaic power extraction for households with PV systems; the effectiveness of the proposed algorithm is validated using Apmds datasets and Pecans Street datasets.

Keywords: non-intrusive load decomposition; net load power; density peak clustering; simplified hidden Markov model; radiation proxy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/18/4935/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/18/4935/ (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:jeners:v:18:y:2025:i:18:p:4935-:d:1751013

Access Statistics for this article

Energies is currently edited by Ms. Cassie Shen

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