ABSTRACT
In recent times, numerous challenges and constraints have been faced by rural areas for achieving continuous power supply. This is overcome through the aid of hybrid energy microgrids. This system integrates artificial intelligence with an optimization algorithm. The proposed structure involves the use of a solar energy system which helps to meet the demand when the traditional system fails to operate. The adaptive control of components with real-time monitoring in the microgrid is implemented using machine learning. This involves control mechanisms such as predictive modelling of the solar power systems. The solar energy output is obtained exactly using ant colony optimization techniques. This helps to evaluate the historical data to forecast the optimum solar energy output. This helps to overcome the various fluctuations in the power supply and thus provide a stable power supply. The energy obtained from the electrical grid and renewable energy system is evenly utilized in the microgrid through these machine learning algorithms. In rural electrification, the proposed system functions in diverse ways. First the numerous constraints and intermittency issues are resolved then reduces the operational costs through ML algorithms. The energy loss in the microgrid is reduced using optimum management of energy flow and storage within the microgrid. Thus the proposed system provides an innovative approach to adopting sustainable rural electrification.
