IOT enabled greenhouse automatic control system for energy efficiency optimization.

Agricultural greenhouses provide optimal conditions for plant growth, but they consume an excessive amount of energy, making energy the second-largest expense after labour costs. Most of the energy is used for heating, which is a major contributor to the high energy demand of the system. Precise and timely control technology can help reduce energy costs and increase profitability. The integration of IoT into greenhouses is a new development in smart agriculture that has the potential to optimise energy use. Various methods exist for optimising energy use in greenhouses, including the use of phase change materials, efficient greenhouse construction designs, and control systems. However, smart automatic control systems are an efficient method that has not been explored enough. Understanding the control algorithm and its proper implementation for use in the greenhouse control system is critical for energy optimisation. This thesis makes three main contributions to greenhouse temperature control. First, a dynamic, physics-based model of greenhouse temperature was optimised to be adaptable for greenhouses equipped with IoT hardware. Second, two control algorithms were implemented in simulation to regulate the system to the grower's desired temperature, while four other control algorithms were implemented to evaluate their energy minimization capability. Results showed that the MPC controller was the best controller in terms of energy savings. Nevertheless, for small to medium greenhouse operators who may have limited resources, relatively simple on-off control algorithm is cost-effective. Finally, the study demonstrates that an IoT-based control system can optimise the energy use in the greenhouse. The use of IoT technology has the capacity to overcome the greenhouse energy management problem with a distribution control system aided by cloud computing. This study demonstrates the potential of IoT-based control systems to save energy and improve greenhouse efficiency by reducing delays and increasing control effectiveness.