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Aggregated smart meter data driven occupant behavior analysis based on inverse problem optimization
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DOI：10.1016/j.enbuild.2025.116074.png)
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Abstract
En 中文
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.
Keywords:
Energy modeling
Smart meters
Residential sector
Occupant behavior
Parameter calibration
Energy demand estimation
Data-driven analysis
Sustainability
Climate goals
Data integration
Journal
IF:
7.1
Papers:
1.6W
Citations:
6.8W
Organization
No organization information available
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