# Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art
## 3 Digital AI Models for Energy Management in Greenhouses  
### Energy Demand in Smart Greenhouses and Energy Optimization Algorithms  
The adoption of digital models (such as MATLAB/Simulink) to regulate the insulated and transparent greenhouse microclimate is an emerging area of research [139]. The materials for greenhouse structures, such as fiberglass, PVC, polycarbonate, and reinforced plastics, had a domino effect on energy use [140]. Intelligent greenhouse structures are energy-intensive--a factor that may impede commercial adoption of AI technologies [141; 142]. The claims made by [77; 78] concerning the energy-intensive nature of greenhouse cultivation were in tandem with [PERSON] et al., who noted that energy costs accounted for 25% of the greenhouse overhead costs [10]. Energy is needed to heat and cool greenhouse structures and regulate light, humidity, and soil moisture. The disproportionate use of energy in greenhouses relative to other sectors in the agricultural industry may explain why \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Optimizers** & **Learning Rates** & **Training Set Accuracy (\%)** & **Test Set Accuracy (\%)** \\ \hline Nadam & 0.001 & 96 & 89 \\ Adamax & 0.001 & 93 & 89 \\ RMSprop & 0.001 & 95 & 89 \\ Adam & 0.001 & 96 & 90 \\ Adam & 0.0001 & 89 & 86 \\ Adam & 0.01 & 91 & 85 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Relationship between optimizers, learning rates, the training set accuracy, and test set accuracy [111]. there was renewed interest in developing the next generation of AI-based energy-saving technologies to reduce energy expenditure and improve yields. [PERSON] et al. documented various bio-inspired interventions for energy optimization in smart grids, smart homes, and the internet of energy [55]. The study suggested that swarm and evolutionary-based optimization algorithms were ideal for optimizing the use of traditional and renewable energy sources to achieve higher user comfort and energy demand aligned with the production capacity and grid demand. In contrast to [PERSON] et al. [55], [PERSON] et al. [142] proposed an AI-based mode based on a particle swarm optimization (PSO) scheme. The latter reduced the cost of greenhouse energy through integrating photovoltaics and dynamic pricing (real-time and time-of-use). The algorithm enabled seamless communication between the external power grid (EPG) and the PV system. For example, the supply of power from the external power grid was only required when sunlight was low. If there was sufficient light and load, the EPG supply was disconnected. In real-life greenhouse operations, the proposals made by [PERSON] et al. [55] and [PERSON] et al. [142] may be relevant or less relevant depending on the local operating conditions, energy sources, the scale of greenhouse agriculture, and specific circumstances; this is in line with [PERSON] et al. who recommended the use of evolutionary algorithms to address the shortcomings associated with proportional-Integral-derivative (PID) control of greenhouse structures. Traditionally, PID was preferred owing to its great performance, simplicity, and flexibility [10]. However, there were certain tradeoffs, including time-consuming computing and the inability to handle abrupt and external disturbances [10]. The case for the PID greenhouse control strategy advanced by [10] corroborates [20], who noted it was a low-cost alternative. However, there were alternative systems with comparable performance, including GA-based model predictive control systems. The PID-related shortcomings partly explain why AI bio-inspired algorithms, such as EA (Evolutionary Algorithm), were used to modify PID controllers in greenhouses. Similarly to [PERSON] et al. [10] and [PERSON] et al. [142], [PERSON] et al. [143] confirmed that bio-inspired algorithms, particularly adaptive chaotic ACO algorithms, could achieve significant energy savings.