Machine Learning Pipeline for Energy and Environmental Prediction in Cold Storage Facilities

As energy demands and costs rise, enhancing energy efficiency in Food and Drink Cold Storage (FDCS) rooms is important for reducing expenses and achieving environmental sustainability ambitions. Forecasting electricity use in FDCSs can help optimise operations and minimise energy consumption by enabling door opening frequency, maintenance, and restocking to be better scheduled. Although Machine Learning (ML) has been applied to forecast energy use in various domains such as commercial and residential buildings, its use in addressing the specific challenges of FDCS, which require stringent temperature and humidity control for food safety and quality, has been less explored. This work addresses this gap by proposing a tailored ML pipeline for FDCS settings capable of predicting one-week into the future and is suitable for small dataset sizes. It provides comparative analysis by employing two distinct real-world FDCS datasets for training, validation, and testing of the developed models. Moreover, in contrast to existing studies predominantly concerned with energy consumption prediction, this study includes the forecasting of indoor temperature and humidity, given their essential role in preserving the quality and longevity of stored food items. Ensemble-based methods, particularly Random Forest, excelled and achieved the lowest electricity MAEs of 150.65 and 384.88 for each dataset, respectively.