Benchmark of Electricity Consumption Forecasting Methodologies Applied to Industrial Kitchens

Abstract

Even though Industrial Kitchens (IKs) are among the highest energy intensity spaces, very little work has been done to forecast their consumption. This work explores the possibility of increasing the accuracy of the consumption forecast in an IK by forecasting disaggregated appliance consumption and comparing these results with the forecast of the total consumption of these appliances (Virtual Aggregate—VA). To do so, three different methods are used: the statistical method (Prophet), classic Machine Learning (ML) method such as random forest (RF), and deep learning (DL) method, namely long short-term memory (LSTM). This work uses individual appliance electricity consumption data collected from a Portuguese restaurant over a period of four consecutive weeks. The obtained results suggest that Prophet and RF are the more viable options. The former achieved the best performance in aggregated data, whereas the latter showed better forecasting results for most of the individual loads. Regarding the performance of the VA against the sum of individual appliance forecasts, all models perform better in the former. However, the very small difference across the results shows that this is a viable alternative to forecast aggregated consumption when only individual appliance consumption data are available.

Keywords

• industrial kitchen
• load forecasting
• hour-ahead
• appliances
• aggregate
• prophet