## 1 Introduction  
Due to the enormous increase and instability of oil and derivatives markets, countries are constantly looking for alternative sources of energy to ensure the independence of their economies from fluctuations in oil prices. Photovoltaic energy can have undeniable advantages, especially due to its cleanliness and low cost. In addition, it can be used in various applications such as in agronomy, where different variables, e.g., temperature and humidity, have to be monitored and controlled. These include temperature and humidity. A greenhouse is a known solution for protecting plant cover from diseases and bad weather. A greenhouse is a complex system, the internal climate of which is influenced by many factors, such as wind speed, solar radiation, external temperature, and humidity. Two main problems have limited the expansion of greenhouse agricultural production. Firstly, control over the indoor climate is an important aspect in achieving microclimate comfort for plant growth. Many research activities have focused on controlling the indoor climate of a greenhouse with different strategies. Predictive neural control [1] has been developed to optimize the greenhouse climate, while a fuzzy controller that was developed to describe a dynamic model in MATLAB/Simulink was described in Reference [2]. In addition, Reference [3] studied several PI control structures that showed strong stationary performance. In Reference [4], the authors introduced a decentralized decoupled fuzzy logic controller (FLC), showing its usefulness in comparison to the conventional PIDmethod, but they did not take into account the effect of ventilation on temperature. References [5; 6] proposed a neuro-fuzzy controller to identify the optimal conditions for plant production and to improve control over the indoor climate. Genetic algorithms implemented in a control system for irrigation in a greenhouse were proposed in References [7; 8], while Reference [9] presented a comparative study of two types of fuzzy multivariate controllers to show their advantages and disadvantages. Reference [10] developed four control techniques to adjust the air temperature inside a greenhouse to a desired value: fuzzy logic control (FLC), an adaptive neuro-fuzzy inference system (ANFIS), artificial neural network control (ANNC), and IP Control. ANFIS [11] and FLC [12] are two of the best known and most used controllers for nonlinear and complex processes such as greenhouses. Reference [13] presented a fuzzy controller with a correlation between the parameters. An FLC was used, in this paper, for the dynamic model of an experimentally validated agricultural greenhouse, with the aim of promoting a suitable microclimate with appropriate actuators installed into the greenhouse. Secondly, the use of several controlled actuators, such as a ventilation system, a heating system, and a humidification/dehumidification system, makes a greenhouse an energy-intensive consumer. Therefore, it is mandatory to use efficient energy systems in order to reduce operating costs. Many researchers have studied control strategies for ventilation and heating systems, for humidification/dehumidification systems, and for the regulation of other agricultural greenhouse parameters. References [14; 15] proposed a ventilation system based on an on-off control, Reference [16] studied a natural ventilation system for an agricultural greenhouse, Reference [17] presented an evaluation of the use of various renewable energy sources to heat a greenhouse, and Reference [18] introduced different fields of application for renewable energy in buildings, in particular in the agricultural sector. Reference [19] proposed energy reduction measured through low-cost and solar energy-based sensors. Reference [20] used direct torque control (DTC) to manage the operation of a motor driving a fan, while [21] discussed the application of a photovoltaic (PV) system to power a temperature control system in an agricultural greenhouse.