Optimizing energy systems of livestock farms with computational intelligence for achieving energy autonomy - Tryhuba, Anatoliy; Hutsol, Taras; Čėsna, Jonas; Tryhuba, Inna; Mudryk, Krzysztof; Francik, Sławomir; Kukharets, Savelii; Mishchenko, Ivan; Oliinyk, Roman (Scientific reports, 2025)
The relevance of the study is due to the need to increase the energy autonomy of livestock farms by introducing innovative solutions based on computational intelligence. Given the significant energy consumption by livestock farms, as well as the reduced dependence on traditional energy sources, there is a need to optimise energy systems using renewable sources. The aim of the research is to develop a model for integrating computational intelligence to optimise energy systems of livestock farms to achieve their energy autonomy. The use of computational models will allow farmers to manage energy consumption more efficiently, minimise carbon emissions, and increase the overall stability of energy supply. The object of research is the energy systems of livestock farms, including traditional and renewable energy sources. The subject of the study is models and methods of optimisation based on computational intelligence used for energy resource management. The paper develops a model for optimising energy systems of livestock farms using a genetic algorithm that involves the systematic implementation of 5 steps. In contrast to traditional static models, the proposed model takes into account the possibility of dynamic adaptation of the structure of the energy supply system to real production conditions. This is done by taking into account the energy demand of livestock farms, the possibility of using renewable energy sources, and external factors such as power grid failures and weather conditions. The proposed model is based on a multi-criteria optimisation approach that simultaneously reduces CO₂ emissions, reduces energy costs and increases the energy sustainability of livestock farms. The genetic algorithm used in the model provides flexible parameter settings and the search for an optimal solution in the context of a variable and complex structure of the energy system. Based on the model, a Python 3.10 program was created to perform labour-intensive calculations for optimising the energy systems of livestock farms. According to the results of testing at the farm of Volyn Nova LLC (Volyn region, Ukraine), it was found that the implementation of the optimised energy system allows reducing CO₂ emissions from 1263 kg/day to 92.3 kg/day and increasing the use of renewable energy sources. Prospects for further research include the adaptation of the model to other types of livestock farms, as well as the development of integration solutions for the combined use of several renewable energy sources.