Malefors, Christopher
- Department of Energy and Technology, Swedish University of Agricultural Sciences
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
Eriksson, Mattias
- Department of Energy and Technology, Swedish University of Agricultural Sciences
- Department of Energy and Technology, Swedish University of Agricultural Sciences
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