@misc{10481/106543,
year = {2025},
month = {11},
url = {https://hdl.handle.net/10481/106543},
abstract = {Feature engineering (FE) plays a crucial role in Machine Learning pipelines, yet it remains a time-consuming
process requiring heavy domain expertise. While Automated Machine Learning (AutoML) has automated model
selection and hyperparameter tuning, it often overlooks FE, which is particularly needed in specialised domains
such as Energy Consumption Forecasting (ECF). To address this limitation, we introduce AutoEnergy, a novel,
domain-aware FE algorithm tailored for ECF. AutoEnergy automatically generates interpretable features from
timestamps and past consumption values through rule-based transformations, integrating them with AutoML
for fully automated ECF modelling while reducing human intervention. The performance of AutoEnergy was
evaluated using eighteen diverse real-world energy consumption datasets spanning residential, commercial, industrial, and grid power domains. Through extensive benchmarking against baseline AutoML without FE and established FE methods, namely TSFresh (with TSEfficient and TSMinimal configurations) and FeatureTools (FT),
AutoEnergy demonstrated significant improvements in both predictive accuracy and computational efficiency.
AutoEnergy achieved forecasting error reductions of 19.52 % to 84.72 % compared to benchmarking methods,
with strong performance on smaller datasets and statistical validation via Friedman and Wilcoxon tests. AutoEnergy demonstrated notable computational efficiency by running 1.31 and 4.41 times faster than FT and TSEff,
respectively. Although 1.58 times slower than TSMin, AutoEnergy achieved 82.38 % lower forecasting errors.
Integrating AutoEnergy with the state-of-the-art Tabular Prior Data Fitted Network (TabPFN) resulted in significant forecasting error reductions across test sets. These findings highlight AutoEnergy’s potential to improve
AutoML performance while reducing reliance on domain expertise for FE, paving the way for fully automated
ML pipelines in ECF applications.},
organization = {University of Nottingham (Grant EP/S023305/1)},
organization = {European Union Next Generation - Ministry for Digital Transformation and the Civil Service (TSI100927-2023-1 Project)},
organization = {María Zambrano Fellowship - Next Generation - Spanish Ministry of Universities},
publisher = {Elsevier B.V.},
keywords = {Automated feature engineering},
keywords = {Automated machine learning},
keywords = {Automl},
title = {AutoEnergy: An automated feature engineering algorithm for energy consumption forecasting with AutoML},
doi = {10.1016/j.knosys.2025.114300},
author = {Alkhulaif, Nasser and L. Bowler, Alexander and Pekaslan, Direnc and Watson, Nicholas J. and Triguero, Isaac},
}