Controller of Heat Point Automation with Microclimate Parameter Control Subsystems under Hybrid Power Supply Conditions in a Greenhouse Complex

This paper focuses on improving the energy efficiency of greenhouse farming in the context of modern challenges related to decarbonization and the implementation of renewable energy sources. The study examines technologies for intelligent greenhouse microclimate control, including sensor systems, automated control algorithms, and hybrid power supply schemes based on solar energy. The authors propose innovative solutions to enhance the energy autonomy of greenhouse complexes, which contribute to reducing operational costs and ensuring the resilience of agricultural production. Special attention is paid to the development of hybrid power supply systems that combine conventional grid sources with renewable energy (solar panels). The proposed model allows the sale of surplus electricity to the grid, further reducing operational expenses and increasing the financial efficiency of the project. The study explores the energy autonomy and reliability of the system by integrating different power sources. Generalized structural diagram of the system is presented, incorporating key energy supply and microclimate management units. Detailed description of the automation controller functional scheme is provided, which ensures efficient energy distribution, microclimate stability, and minimizes human intervention. The analysis of the energy consumption of key greenhouse systems (heating, cooling, and lighting) under various microclimate control methods was conducted. The results indicate that the introduction of intelligent regulation systems significantly reduces energy consumption: by 30…40 % when using smart regulation algorithms; by 50…60 % with the implementation of comprehensive IoT solutions based on neural networks and genetic algorithms. The proposed microclimate control subsystem includes sensor blocks, a microcontroller module, actuator control units, and signal processing and conversion modules. A detailed description of each functional component, its role, and interactions is provided. The research also includes the economic analysis of the payback period for the proposed solutions. It was determined that transitioning from traditional management methods to intelligent and IoT-based systems can reduce electricity costs by up to 60 % and ensure a payback period within 0.3…1.5 years, depending on implementation complexity. The results of this study can be used to modernize greenhouse complexes, optimize energy consumption in the agricultural sector, and promote the sustainable development of greenhouse farming.