Evaluation of CFD and Machine Learning Methods on Predicting Greenhouse Microclimate Parameters with the Assessment of Seasonality Impact on Machine Learning Performance.

[1]  A. Allouhi,et al.  Machine learning algorithms to assess the thermal behavior of a Moroccan agriculture greenhouse , 2021, Cleaner Engineering and Technology.

[2]  A. Bermak,et al.  Energy utilization assessment of a semi-closed greenhouse using data-driven model predictive control , 2021, Journal of Cleaner Production.

[3]  Abdellah Mechaqrane,et al.  Evaluation of Supervised Learning Models in Predicting Greenhouse Energy Demand and Production for Intelligent and Sustainable Operations , 2021, Energies.

[4]  M. Jazouli,et al.  CFD Study of Airflow and Microclimate Patterns Inside a Multispan Greenhouse , 2021 .

[5]  Abdellah Mechaqrane,et al.  Estimation of daily global solar radiation using empirical and machine-learning methods: A case study of five Moroccan locations , 2021, Sustainable Materials and Technologies.

[6]  Wu Wang,et al.  Reliable solar irradiance prediction using ensemble learning-based models: A comparative study , 2020 .

[7]  Seyed Majid Sajadiye,et al.  The effect of dynamic solar heat load on the greenhouse microclimate using CFD simulation , 2019, Renewable Energy.

[8]  Zhenzhi Lin,et al.  Data-Driven Load Forecasting of Air Conditioners for Demand Response Using Levenberg-Marquardt Algorithm-Based ANN , 2019, Big Data Cogn. Comput..

[9]  H. Oktay,et al.  An Artificial Neural Network Model to Predict the Thermal Properties of Concrete Using Different Neurons and Activation Functions , 2019, Advances in Materials Science and Engineering.

[10]  B. Mohammadi,et al.  Application of dynamic model to predict some inside environment variables in a semi-solar greenhouse , 2018, Information Processing in Agriculture.

[11]  Abbas Rohani,et al.  Applied machine learning in greenhouse simulation; new application and analysis , 2018, Information Processing in Agriculture.

[12]  A Aleksandra Sretenovic,et al.  Support vector machine for the prediction of heating energy use , 2018 .

[13]  Muhammed A. Hassan,et al.  Exploring the potential of tree-based ensemble methods in solar radiation modeling , 2017 .

[14]  D. Khare,et al.  Application of artificial intelligence to estimate the reference evapotranspiration in sub-humid Doon valley , 2017, Applied Water Science.

[15]  Cătălin George Popovici,et al.  HVAC System Functionality Simulation Using ANSYS-Fluent☆ , 2017 .

[16]  Abbas Rohani,et al.  Heat transfer and MLP neural network models to predict inside environment variables and energy lost in a semi-solar greenhouse , 2016 .

[17]  T. Rocksch,et al.  USING ARTIFICIAL NEURAL NETWORKS TO PREDICT THE CLIMATE IN A GREENHOUSE: FIRST SIMULATION RESULTS ON A SEMI-CLOSED SYSTEM , 2015 .

[18]  Zhengwei Li,et al.  Using Support Vector Machine to Predict Next Day Electricity Load of Public Buildings with Sub-metering Devices☆ , 2015 .

[19]  Carlos Cardeira,et al.  The Daily and Hourly Energy Consumption and Load Forecasting Using Artificial Neural Network Method: A Case Study Using a Set of 93 Households in Portugal☆ , 2014 .

[20]  J. Ríos-Moreno,et al.  Greenhouse energy consumption prediction using neural networks models , 2009 .

[21]  Diego L. Valera,et al.  Measurement and simulation of climate inside Almerı́a-type greenhouses using computational fluid dynamics , 2004 .

[22]  Gilles Trystram,et al.  Neural networks for the heat and mass transfer prediction during drying of cassava and mango , 2004 .

[23]  Jan G. Pieters,et al.  Modelling greenhouse temperature using system identification by means of neural networks , 2004, Neurocomputing.

[24]  António E. Ruano,et al.  Neural network models in greenhouse air temperature prediction , 2002, Neurocomputing.

[25]  Harinder P. Singh,et al.  An introduction to artificial neural networks , 2001, astro-ph/0102224.

[26]  Jan Pieters,et al.  Performances of Greenhouses with the Presence of Condensation on Cladding Materials , 1997 .