@misc{10481/96107,
year = {2024},
month = {10},
url = {https://hdl.handle.net/10481/96107},
abstract = {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.},
organization = {Engineering and Physical Research Council (EPSRC) [Grant number EP/S023305/1] for the University of
Nottingham, EPSRC Centre for Doctoral Training in Horizon: Creating Our Lives in Data},
organization = {Maria Zambrano
Senior Fellowship at the University of Granada},
organization = {R&D and Innovation project with reference
PID2020-119478GB-I00 granted by Spain’s Ministry of Science and Innovation and European Regional Development Fund (ERDF)},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
keywords = {Energy Forecasting},
keywords = {Feature Engineering},
keywords = {Food and Drink Cold Storage Rooms},
title = {Machine Learning Pipeline for Energy and Environmental Prediction in Cold Storage Facilities},
doi = {10.1109/ACCESS.2024.3482572},
author = {Alkhulaifi, Nasser and Bowler, Alexander and Pekaslanc, Direnc and Serdaroglu, Gulcan and Closs, Steve and Watson, Nicholas J.},
}