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 $<$0.15 for the trained dataset. Economic savings were obtained by forecasting compared with a no-plan scenario, supporting selection of the random forest approach for effective forecasting of meal planning. Overall, the results obtained using forecasting models for meal planning in times of crisis confirmed their usefulness. Continuous use can improve estimates for the test period, due to the agile and flexible nature of these models. This is particularly important when guest attendance is unpredictable, so that production planning can be optimized to reduce food waste and contribute to a more sustainable and resilient food system.

在任何正常情况下，食物浪费都是公共餐饮场所的一个重大问题。2020 年春季，Covid-19 大流行给公共餐饮系统带来了更大的压力，暴露了系统内部的弱点以及由于快速变化的消费模式而产生的食物浪费。在危机时期，节约资源并将现有资源分配到最能发挥作用的领域尤为重要，但这也带来了重大挑战。本研究评估了预测模型在大流行开始和整个大流行期间预测客人出席情况的潜力。这是通过收集大流行之前和期间瑞典学校和学前餐饮机构的客人出勤数据来完成的，并使用机器学习方法根据历史数据预测未来的客人出席情况。各种学习方法的比较表明，随机森林比简单的人工神经网络产生更准确的预测，条件平均绝对预测误差为$<$训练数据集为 0.15。与无计划情景相比，通过预测获得经济节约，支持选择随机森林方法来有效预测膳食计划。总体而言，在危机时期使用膳食计划预测模型获得的结果证实了它们的有用性。由于这些模型的敏捷性和灵活性，持续使用可以改进对测试期的估计。当来宾出勤率不可预测时，这一点尤为重要，这样可以优化生产计划以减少食物浪费，并有助于建立更具可持续性和弹性的食物系统。