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Abstract

Food waste is a significant problem within public catering establishments in any normal situation. During spring 2020 the Covid-19 pandemic placed the public catering system under greater pressure, revealing weaknesses within the system and generation of food waste due to rapidly changing consumption patterns. In times of crisis, it is especially important to conserve resources and allocate existing resources to areas where they can be of most use, but this poses significant challenges. This study evaluated the potential of a forecasting model to predict guest attendance during the start and throughout the pandemic. This was done by collecting data on guest attendance in Swedish school and preschool catering establishments before and during the pandemic, and using a machine learning approach to predict future guest attendance based on historical data. Comparison of various learning methods revealed that random forest produced more accurate forecasts than a simple artificial neural network, with conditional mean absolute prediction error of

Keywords

Food waste school kitchens forecasting random-forest system optimization

Published in

Socio-Economic Planning Sciences
2022, volume: 82, number: Part A, article number: 101041

SLU Authors

Associated SLU-program

Food Waste

Global goals (SDG)

SDG12 Responsible consumption and production

UKÄ Subject classification

Other Natural Sciences
Business Administration
Information Systems

Publication identifier

  • DOI: https://doi.org/10.1016/j.seps.2021.101041

Permanent link to this page (URI)

https://res.slu.se/id/publ/110958