Multi-Criteria Genetic Algorithm for Optimization and Interval Forecasting of Autonomous Photovoltaic Systems

This article investigates algorithmic approaches for analyzing and predicting the operating modes of small photovoltaic power plants. A multi-criteria genetic algorithm is analyzed and developed, applicable to short data series and variable operating modes typical of energy systems. Peak demand, which occurs during a limited number of hours, represents a significant challenge for the overall energy balance and grid stability. The task under consideration is formulated as a problem in which the forecast error and the model uncertainty measure are simultaneously minimized. For this purpose, an interval forecasting scheme based on fuzzy regression is used, with its parameters optimized by an evolutionary mechanism. The proposed multi-criteria genetic algorithm is an effective tool for the parametric adaptation of forecasting models. It lays the groundwork for implementation in monitoring and control systems for autonomous photovoltaic installations, thereby enhancing energy efficiency and optimizing performance through real-time data analysis and adaptive decision-making. The obtained results show improved robustness and reliability of the forecast compared to classical approaches for short time series.