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CC BY V4.0, Open Access
Journal article
Hybrid deep learning models for water demand forecasting in greenhouses: Exploring the energy Nexus in Urban agriculture
Energy nexus, Vol.20, 100546
2025
Abstract
Precise water demand forecasting (WDF) is crucial for sustainable irrigation and resource efficiency in urban greenhouse systems. This study introduces a cutting-edge hybrid deep learning approach designed for short-term WDF, while also considering the energy nexus between water, energy, and environmental factors. The model integrates the least squares generative adversarial network (LSGAN) for data pre-processing and noise reduction, convolutional neural networks (CNN) for feature selection, and bidirectional long short-term memory (BiLSTM) for time-series state modeling, and named as LSGAN-CBiLSTM. Using real-world data from the Wageningen Research Centre in Bleiswijk, Netherlands, the model significantly outperformed benchmark approaches, achieving an R-value of 99.57 % with minimal forecasting errors. The model demonstrated exceptional stability, minimal bias, and strong handling of environmental variability, improving short-term WDF accuracy, optimizing water management in urban agriculture, enhancing sustainable irrigation, and addressing the energy nexus for efficient resource use. [Display Omitted]
Details
- Title
- Hybrid deep learning models for water demand forecasting in greenhouses: Exploring the energy Nexus in Urban agriculture
- Authors/Creators
- Arash MoradzadehLazhar Ben-BrahimAli ArefiArman OshnoeiS.M. Muyeen
- Publication Details
- Energy nexus, Vol.20, 100546
- Publisher
- Elsevier Ltd.
- Number of pages
- 13
- Identifiers
- 991005819115807891
- Copyright
- © 2025 The Author(s).
- Murdoch Affiliation
- Centre for Water, Energy and Waste
- Language
- English
- Resource Type
- Journal article
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