Khalid Bouali
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Khalid Bouali has a total of 24 co-authors. Here are the top co-authors by number of shared publications:
- Abderrahim Bajit
Ibn Tofail University
9 shared publications - Hamza Benzzine
Advanced Systems Engineering Laboratory-ISA, National School of Applied Sciences-ENSA, Ibn Tofail University
7 shared publications - Hicham Essamri
Ibn Tofail University
5 shared publications - Rachid El Bouayadi
National School of Applied Sciences, Ibn Tofail University
5 shared publications Rachid El Bouayadi
National School of Applied Sciences -ENSA-, Ibn Tofail University
4 shared publications- Hicham Labrim
Advanced Systems Engineering Laboratory, Ibn Tofail University, Kenitra 14000, Morocco
3 shared publications
Latest Publications
- Open Access
Integrated Passive Cooling Techniques for Energy-Efficient Greenhouses in Hot–Arid Environments: Evidence from a Systematic Review
This systematic review synthesizes passive and passive-first cooling strategies for greenhouses in hot–arid climates, organizing evidence across four domains: Airflow & Ventilation, Shading & Radiative Control, Thermal Storage & Ground Coupling, and Structural Design & Geometry. Drawing on the project corpus, we analyze 10–13 distinct techniques including ridge and side natural ventilation, windcatchers and solar chimneys, external shade nets, NIR-selective and transparent radiative-cooling films, and dynamic PV shading; earth-to-air heat exchangers (EAHE/GAHT), rock-bed sensible storage, phase-change materials (PCMs), and sunken or buried envelopes; as well as roof slope and shape, span number, and orientation. Across studies, cooling outcomes are reported as peak or daytime indoor air temperature reductions, defined relative either to outdoor conditions or to a control greenhouse, with the reference frame and temporal aggregation specified in the synthesis. Typical outcomes include ≈3–7 °C daytime reduction for optimized ventilation, ≈2–4 °C for shading and spectral covers while preserving PAR, ≈5–7 °C intake cooling for EAHE with winter pre-heating, and up to ≈14 °C peak attenuation for rock-bed storage under favorable conditions. Structural choices consistently amplify these effects by sustaining pressure head and limiting thermal heterogeneity. Performance is strongly context-dependent—governed by wind regime, diurnal amplitude, dust and UV exposure, and crop-specific light and temperature thresholds—and the most robust results arise from stacked, site-specific designs that combine skin-level radiative rejection, buoyancy-supportive geometry, and ground or latent buffering with minimal active backup. Smart controllers that modulate vents, shading, and targeted fogging or fans based on VPD or temperature differentials improve stability and reduce water and energy use by engaging actuation only when passive capacity is exceeded. We recommend standardized composite metrics encompassing temperature moderation, humidity stability, PAR availability, and water and energy use per unit yield to enable fair cross-study comparison, multi-season validation, and policy adoption. Collectively, the synthesized techniques provide a practical palette for improved greenhouse climate management under hot and arid conditions.
Open AccessIntegrated Passive Cooling Techniques for Energy-Efficient Greenhouses in Hot–Arid Environments: Evidence from a Systematic Review
This systematic review synthesizes passive and passive-first cooling strategies for greenhouses in hot–arid climates, organizing evidence across four domains: Airflow & Ventilation, Shading & Radiative Control, Thermal Storage & Ground Coupling, and Structural Design & Geometry. Drawing on the project corpus, we analyze 10–13 distinct techniques including ridge and side natural ventilation, windcatchers and solar chimneys, external shade nets, NIR-selective and transparent radiative-cooling films, and dynamic PV shading; earth-to-air heat exchangers (EAHE/GAHT), rock-bed sensible storage, phase-change materials (PCMs), and sunken or buried envelopes; as well as roof slope and shape, span number, and orientation. Across studies, cooling outcomes are reported as peak or daytime indoor air temperature reductions, defined relative either to outdoor conditions or to a control greenhouse, with the reference frame and temporal aggregation specified in the synthesis. Typical outcomes include ≈3–7 °C daytime reduction for optimized ventilation, ≈2–4 °C for shading and spectral covers while preserving PAR, ≈5–7 °C intake cooling for EAHE with winter pre-heating, and up to ≈14 °C peak attenuation for rock-bed storage under favorable conditions. Structural choices consistently amplify these effects by sustaining pressure head and limiting thermal heterogeneity. Performance is strongly context-dependent—governed by wind regime, diurnal amplitude, dust and UV exposure, and crop-specific light and temperature thresholds—and the most robust results arise from stacked, site-specific designs that combine skin-level radiative rejection, buoyancy-supportive geometry, and ground or latent buffering with minimal active backup. Smart controllers that modulate vents, shading, and targeted fogging or fans based on VPD or temperature differentials improve stability and reduce water and energy use by engaging actuation only when passive capacity is exceeded. We recommend standardized composite metrics encompassing temperature moderation, humidity stability, PAR availability, and water and energy use per unit yield to enable fair cross-study comparison, multi-season validation, and policy adoption. Collectively, the synthesized techniques provide a practical palette for improved greenhouse climate management under hot and arid conditions.
- 2 January 2026
- Published by Springer Nature
- p. 260-270
A Hybrid LSTM-Transformer-Based MPC Framework for Efficient Greenhouse Climate Forecasting and Control
- 2 January 2026
- Published by Springer Nature
- p. 260-270
A Hybrid LSTM-Transformer-Based MPC Framework for Efficient Greenhouse Climate Forecasting and Control
- 2 January 2026
- Published by Springer Nature
- p. 92-103
Edge AI-Enhanced Nodes for Intelligent Agricultural Greenhouse Microclimate Control
- 2 January 2026
- Published by Springer Nature
- p. 92-103
Edge AI-Enhanced Nodes for Intelligent Agricultural Greenhouse Microclimate Control
- 2 January 2026
- Published by Springer Nature
- p. 503-515
AI-Driven Dynamic Sensor Optimization in Greenhouse IoT: Energy Efficiency with Edge-Fog Computing and Time Series Forecasting
- 2 January 2026
- Published by Springer Nature
- p. 503-515
AI-Driven Dynamic Sensor Optimization in Greenhouse IoT: Energy Efficiency with Edge-Fog Computing and Time Series Forecasting
- 25 November 2025
- Published by Springer Nature
- p. 83-97
An Optimized and Intelligent Edge Computing to Enhance Agricultural Sustainability in Intelligent Farming
- 25 November 2025
- Published by Springer Nature
- p. 83-97
An Optimized and Intelligent Edge Computing to Enhance Agricultural Sustainability in Intelligent Farming
- 25 November 2025
- Published by Springer Nature
- p. 35-47
An Innovative Hardware and Software Design of Green, Intelligent, Secured and Sustainable Farming IOT Node
- 25 November 2025
- Published by Springer Nature
- p. 35-47
An Innovative Hardware and Software Design of Green, Intelligent, Secured and Sustainable Farming IOT Node
- 30 October 2025
- Published by Springer Nature
- p. 459-471
Predictive Control for Optimal Greenhouse Micro-Climate Management: An LSTM-Based Approach
- 30 October 2025
- Published by Springer Nature
- p. 459-471
Predictive Control for Optimal Greenhouse Micro-Climate Management: An LSTM-Based Approach
- 30 October 2025
- Published by Springer Nature
- p. 541-551
Intelligent MPC-Based Time-Series Forecasting for Efficient Greenhouse Climate Automation
- 30 October 2025
- Published by Springer Nature
- p. 541-551
Intelligent MPC-Based Time-Series Forecasting for Efficient Greenhouse Climate Automation
- 1 October 2025
- Published by Springer Nature
- p. 315-326
Applied Green Computational Intelligence-Based Solutions to Enhance Sustainability in Precision Farming
- 1 October 2025
- Published by Springer Nature
- p. 315-326
Applied Green Computational Intelligence-Based Solutions to Enhance Sustainability in Precision Farming
- 1 October 2025
- Published by Springer Nature
- p. 163-176
A New Optimized, Green Computational Intelligence, Secure, and Sustainable Solutions in Precision Farming
- 1 October 2025
- Published by Springer Nature
- p. 163-176
A New Optimized, Green Computational Intelligence, Secure, and Sustainable Solutions in Precision Farming