Forecast-Driven Climate Control for Smart Greenhouses: Energy Optimization Using LSTM Model

Abdulaziz Aborujilah, Mohammed Al-Sarem () and Marwan Alabed Abu-Zanona ()
Additional contact information
Abdulaziz Aborujilah: Department of Management Information System, College of Commerce & Business Administration, Dhofar University, Salalah 211, Oman
Mohammed Al-Sarem: College of Computer Science and Engineering, Taibah University, Medina 41477, Saudi Arabia
Marwan Alabed Abu-Zanona: Department of Management Information Systems, King Faisal University, Al-Ahsa 31982, Saudi Arabia

Energies, 2025, vol. 18, issue 21, 1-20

Abstract: Greenhouses play a vital role in modern agriculture by providing controlled environments for year-round crop production. However, climate regulation within these structures accounts for a significant portion of their energy consumption, often exceeding 50% of operational costs. Current greenhouse systems predominantly rely on reactive control strategies, leading to energy inefficiency and unstable internal conditions. Addressing this gap, the present study develops a machine learning-based framework that leverages time series forecasting models—specifically Long Short-Term Memory (LSTM)—that predict key climate parameters and generate optimal actuator control recommendations. The system utilizes multivariate environmental data to forecast temperature, humidity, and CO 2 levels and minimize a composite energy proxy through proactive adjustments to heating, ventilation, and lighting systems. Experimental results demonstrate high prediction accuracy (R 2 = 0.9835) and significant improvements in energy efficiency. By integrating predictive analytics with real-time sensor feedback, the proposed approach supports intelligent, energy-aware decision-making and advances the development of smart agriculture through proactive greenhouse climate management.

Keywords: smart greenhouse; climate control optimization; time series forecasting; machine learning; LSTM neural networks; energy-efficient agriculture (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/21/5821/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/21/5821/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:21:p:5821-:d:1787467

Access Statistics for this article

Energies is currently edited by Ms. Cassie Shen

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().