Collaborative Inference Of Missing Smart Electric Meter Data For A Building
| Autor: | Nan Duan, Jose Cadena, Pedro Sotorrio, Jhi-Young Joo |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry 020209 energy 0211 other engineering and technologies Inference 02 engineering and technology computer.software_genre Missing data Data modeling Hardware_GENERAL Electricity meter 021105 building & construction 0202 electrical engineering electronic engineering information engineering Metre Electricity Data mining Autoregressive integrated moving average Time series business computer |
| Zdroj: | MLSP |
| DOI: | 10.1109/mlsp.2019.8918698 |
| Popis: | This paper proposes a novel approach to infer missing electricity meter data of a building using a seasonal autoregressive integrated moving average model with exogenous variables (SARIMAX). A cross-correlation function is utilized to identify buildings that have electricity usage patterns similar to the building with missing meter data. We train a SARIMAX model using the historic electricity usage of the building with missing data, the weather temperature as an exogenous variable, and electricity usage of buildings identified to be similar to the building of interest, to facilitate collaborative inference of missing meter data. The proposed approach is verified using actual 15-minute interval electricity meter data of four office buildings in Northern California. |
| Databáze: | OpenAIRE |
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