Aggregated smart meter data driven occupant behavior analysis based on inverse problem optimization

Uchida, Hideaki; Kishimoto, Kazumasa; Nishizawa, Kazuki; Shimoda, Yoshiyuki; Yamaguchi, Yohei; Togawa, Kazuya

doi:https://doi.org/10.1016/j.enbuild.2025.116074

Aggregated smart meter data driven occupant behavior analysis based on inverse problem optimization

Uchida, Hideaki; Kishimoto, Kazumasa; Nishizawa, Kazuki et al.

Energy and Buildings, 2025, 345, 116074

Number of Access:34（2025-10-27 10:17 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/102788

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Title	Aggregated smart meter data driven occupant behavior analysis based on inverse problem optimization
Creator	NRID 1000090837387 1000090837387 ORCID 0009-0004-1515-9062 0009-0004-1515-9062 researchmap permalink hideaki_uchida hideaki_uchida Scopus Author ID 57195614241 57195614241 CreatorName Uchida, Hideaki
	CreatorName Kishimoto, Kazumasa
	CreatorName Nishizawa, Kazuki
	NRID 1000020226278 1000020226278 ORCID 0000-0002-4487-0089 0000-0002-4487-0089 researchmap permalink read0014119 read0014119 Scopus Author ID 15081121700 15081121700 CreatorName Shimoda, Yoshiyuki
	NRID 1000040448098 1000040448098 ORCID 0000-0001-6794-5939 0000-0001-6794-5939 researchmap permalink read0140668 read0140668 Scopus Author ID 8638124400 8638124400 CreatorName Yamaguchi, Yohei
	CreatorName Togawa, Kazuya
Subject	Climate goals
	Data integration
	Data-driven analysis
	Energy demand estimation
	Energy modeling
	Occupant behavior
	Parameter calibration
	Residential sector
	Smart meters
	Sustainability
Description	Uchida H., Kishimoto K., Nishizawa K., et al. Aggregated smart meter data driven occupant behavior analysis based on inverse problem optimization. Energy and Buildings 345, 116074 (2025); https://doi.org/10.1016/j.enbuild.2025.116074.
Abstract	This research offers an innovative advancement in bottom-up building energy end-use modeling, vital for residential energy system analysis and planning. Conventional models rely on extensive demographic, behavioral, and equipment data from various sources, but these methods often compromise accuracy. To address this, our study introduces a novel methodology for parameter calibration using half-hourly smart meter data, despite its lower resolution due to capacity, management, and privacy constraints. Incorporating these data enhances the precision of energy demand estimations and accurately reproduces occupant behavior (OB). This method refines model accuracy and identifies of OB across various household types, providing valuable insights for planning targeted energy conservation measures crucial for achieving climate goals. Moreover, it enables the tracking of behavioral changes over time, which traditional statistical methods cannot achieve, amplifying its utility. However, this study recognizes the need for further enhancement through the integration of additional data sources, such as movement data from smartphone applications and sensor-based household measurements. These improvements have the potential to revolutionize energy system analysis, contributing to a more sustainable, carbon-free society.
Publisher	Elsevier B.V.
Source Title	Energy and Buildings
Volume (Issue)	345
Page	116074
Date of Issued	2025-10-15
Language	English
Handle URL	https://hdl.handle.net/11094/102788
PISSN	03787788
Relation.isIdenticalTo	DOI (Publisher Version) https://doi.org/10.1016/j.enbuild.2025.116074
Access Rights	open access
CopyRight	This article is licensed under a Creative Commons Attribution 4.0 International License.
CopyRight	© 2025 The Authors
oaire:version	Version of Record
Category	学術雑誌論文 Journal Article

Resource Type	journal article
Local Type	Journal Article
DCMI Type	Text
DCTERMS.bibliographicCitation	Energy and Buildings. 2025, 345, p. 116074
DC.title	Aggregated smart meter data driven occupant behavior analysis based on inverse problem optimization
DC.creator	Uchida, Hideaki
	Kishimoto, Kazumasa
	Nishizawa, Kazuki
	Shimoda, Yoshiyuki
	Yamaguchi, Yohei
	Togawa, Kazuya
DC.publisher	Elsevier B.V.
DC.language' scheme='DCTERMS.RFC1766	English
DC.type' scheme='DCTERMS.DCMIType	Text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2025-10-15
DC.identifier	https://doi.org/10.1016/j.enbuild.2025.116074
DC.subject	Climate goals
	Data integration
	Data-driven analysis
	Energy demand estimation
	Energy modeling
	Occupant behavior
	Parameter calibration
	Residential sector
	Smart meters
	Sustainability
DC.description	Uchida H., Kishimoto K., Nishizawa K., et al. Aggregated smart meter data driven occupant behavior analysis based on inverse problem optimization. Energy and Buildings 345, 116074 (2025); https://doi.org/10.1016/j.enbuild.2025.116074.
DCTERMS.abstract	This research offers an innovative advancement in bottom-up building energy end-use modeling, vital for residential energy system analysis and planning. Conventional models rely on extensive demographic, behavioral, and equipment data from various sources, but these methods often compromise accuracy. To address this, our study introduces a novel methodology for parameter calibration using half-hourly smart meter data, despite its lower resolution due to capacity, management, and privacy constraints. Incorporating these data enhances the precision of energy demand estimations and accurately reproduces occupant behavior (OB). This method refines model accuracy and identifies of OB across various household types, providing valuable insights for planning targeted energy conservation measures crucial for achieving climate goals. Moreover, it enables the tracking of behavioral changes over time, which traditional statistical methods cannot achieve, amplifying its utility. However, this study recognizes the need for further enhancement through the integration of additional data sources, such as movement data from smartphone applications and sensor-based household measurements. These improvements have the potential to revolutionize energy system analysis, contributing to a more sustainable, carbon-free society.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.rights	© 2025 The Authors
DC.format	102788
citation_title	Aggregated smart meter data driven occupant behavior analysis based on inverse problem optimization
citation_author	Uchida, Hideaki
	Kishimoto, Kazumasa
	Nishizawa, Kazuki
	Shimoda, Yoshiyuki
	Yamaguchi, Yohei
	Togawa, Kazuya
citation_publisher	Elsevier B.V.
citation_language	English
citation_date	2025-10-15
citation_journal_title	Energy and Buildings
citation_volume	345
citation_firstpage	116074
citation_doi	https://doi.org/10.1016/j.enbuild.2025.116074
citation_public_url	https://hdl.handle.net/11094/102788

Aggregated smart meter data driven occupant behavior analysis based on inverse problem optimization

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