Mathematical aspects of the optimal balance of renewable and non-renewable energy resources in energy-intensive technology solutions

The optimization of energy resources in energy-intensive processes represents a critical challenge for advancing sustainability, efficiency, and energy security. Controlled heating applications, such as irrigation water heating in agricultural greenhouses, require particularly high and stable energy input, making them a representative case for testing advanced optimization strategies. This study develops an IT- based framework that integrates renewable and non-renewable energy sources through predictive control mechanisms and dynamic environmental monitoring. The system combines solar thermal and photovoltaic (PV) technologies with conventional heating methods to ensure that critical temperature thresholds are maintained at minimal energy cost. Mathematical modelling and advanced algorithms are employed to predict the evolution of soil moisture, irrigation water temperature, and surrounding environmental conditions. By continuously analysing these dynamic parameters, the framework optimally allocates available energy resources, prioritizing renewables while compensating with non-renewable inputs only when necessary. This predictive balancing reduces fossil fuel dependency, improves operational efficiency, and lowers the environmental footprint of energy-intensive agricultural processes. The results demonstrate that the proposed solution not only enhances greenhouse resource management but also provides a scalable methodology applicable to other temperature-critical systems, such as domestic hot water supply. Overall, the research highlights how predictive IT-based energy management can significantly advance the sustainable operation of energy-intensive processes across diverse sectors.