Zhang, Shaobo, Guo, Xiao and Liu, Ying  ORCID: https://orcid.org/0000-0001-9319-5940
2025.
Enhancing machine learning models for vertical farm energy forecasting: impact of data smoothing and feature selection.
Presented at: 10th International Conference on Cloud Computing and Big Data Analytics,
Chengdu, China,
24-26 April 2025.
Proceedings of the 10th International Conference on Cloud Computing and Big Data Analytics.
IEEE,
pp. 151-160.
10.1109/ICCCBDA64898.2025.11030388
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Abstract
The widespread adoption of vertical farming is constrained by excessive energy consumption, highlighting the need for accurate energy consumption forecasting to develop effective energy-saving strategies. Data-driven models have become increasingly important for this purpose, yet prediction accuracy depends heavily on both data smoothing and feature selection. However, their effects on vertical farm energy estimation remain underexplored. This study examines how different data preprocessing methods and feature selection techniques influence energy cost prediction in vertical farming using data-driven models. Specifically, it compares two data smoothing techniques—Gaussian Kernel Density Estimation and the Savitzky-Golay filter—in the preprocessing stage. Additionally, it evaluates three feature selection methods: backward elimination, PCA-based backward elimination, and genetic algorithms (GA), assessing their impact on model performance. The results indicate that the Savitzky-Golay filter and PCA-based backward elimination significantly enhance both prediction accuracy and computational efficiency. These findings provide valuable insights for optimizing energy efficiency in vertical farming.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Engineering |
| Publisher: | IEEE |
| ISBN: | 979-8-3315-3081-5 |
| ISSN: | 2832-3726 |
| Date of First Compliant Deposit: | 8 August 2025 |
| Date of Acceptance: | 20 March 2025 |
| Last Modified: | 08 Aug 2025 14:45 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/179398 |
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