Industrial kitchen appliance consumption forecasting: Hour-ahead and day-ahead perspectives with post-processing improvements Article Swipe
Vasco Andrade
,
Hugo Morais
,
Lucas Pereira
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.compeleceng.2024.109145
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.compeleceng.2024.109145
Forecasting techniques have gained considerable prominence within the electric energy sector. Many studies have been documented in the literature, addressing various facets of the energy grid, ranging from power generation to end-user consumption. However, it is noteworthy that the prediction of individual appliance demand has remained relatively unexplored despite its increasing significance, particularly in modern power grids characterized by a dominant presence of distributed energy resources. In light of this research gap, this work focuses on developing and evaluating methodologies for forecasting active power consumption at the device level in the context of industrial kitchens. Three post-processing algorithms are also proposed to improve the forecasting accuracy by leveraging historical predictions. A comprehensive case study employing sub-metered data from 15 industrial kitchen devices was conducted to validate the proposed methods, spanning both hour-ahead and day-ahead scenarios. The results demonstrate the effectiveness of the proposed methods in both forecasting horizons, particularly of the post-processing techniques that show average improvements of over 30% in day-ahead and 50% in hour-ahead, compared to the original predictions.
Related Topics
Concepts
Context (archaeology)
Consumption (sociology)
Grid
Energy consumption
Power consumption
Computer science
Look-ahead
Demand forecasting
Industrial engineering
Work (physics)
Engineering
Power (physics)
Operations research
Environmental economics
Electrical engineering
Economics
Social science
Paleontology
Mechanical engineering
Geometry
Physics
Sociology
Mathematics
Quantum mechanics
Algorithm
Biology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.compeleceng.2024.109145
- OA Status
- hybrid
- Cited By
- 3
- References
- 66
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392438271
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392438271Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.compeleceng.2024.109145Digital Object Identifier
- Title
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Industrial kitchen appliance consumption forecasting: Hour-ahead and day-ahead perspectives with post-processing improvementsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-03-01Full publication date if available
- Authors
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Vasco Andrade, Hugo Morais, Lucas PereiraList of authors in order
- Landing page
-
https://doi.org/10.1016/j.compeleceng.2024.109145Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.compeleceng.2024.109145Direct OA link when available
- Concepts
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Context (archaeology), Consumption (sociology), Grid, Energy consumption, Power consumption, Computer science, Look-ahead, Demand forecasting, Industrial engineering, Work (physics), Engineering, Power (physics), Operations research, Environmental economics, Electrical engineering, Economics, Social science, Paleontology, Mechanical engineering, Geometry, Physics, Sociology, Mathematics, Quantum mechanics, Algorithm, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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66Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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