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Forecasting cooling load and water demand of a semi-closed greenhouse using a hybrid modelling approach.pdf (6.86 MB)

Forecasting cooling load and water demand of a semi-closed greenhouse using a hybrid modelling approach

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journal contribution
submitted on 2024-06-27, 04:56 and posted on 2024-06-27, 04:57 authored by Farhat Mahmood, Rajesh Govindan, David Yang, Amine Bermak, Tareq Al-Ansari

Forecasting the greenhouse cooling and water demand is critical for improving the performance, reducing energy consumption, and operating costs throughout the year. This research proposes a hybrid modelling approach by analyzing machine learning models to determine the most suitable algorithm for a semi-closed greenhouse. The models were investigated by increasing the time step, excluding the actuator data history, and using data sets based on different seasons to determine the forecasting accuracy. LSTM outperformed both SVMR and MLP with an RMSE and R2 value of 0.352°C, 0.982 for temperature, and 1.23%, 0.954 for relative humidity. The outputs from LSTM were used as input for the analytical model to forecast the cooling load and water demand. Results illustrated that the greenhouse had a cooling demand of 6.03 and 15.33 MWh for a two-day period in winter and summer. Similarly, the water demand was for winter and summer was 5.85 and 12.48 m3.

Other Information

Published in: International Journal of Ambient Energy
See article on publisher's website:

Additional institutions affiliated with: Hassad Food - Qatar Investment Authority


Qatar National Research Fund (MME01-0922-190049), Developing National Food Security Intelligence.



  • English


Taylor & Francis

Publication Year

  • 2022

License statement

This Item is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • College of Science and Engineering - HBKU
  • Qatar Fertiliser Company Q.P.S.C. (QAFCO)