A Unified Forecasting and Optimization Framework for Sustainable Smart Energy Systems

Abstract

The advancement of reliable and sustainable smart energy systems requires predictive and optimization approaches capable of managing nonlinearities, uncertainties, and multidimensional interactions in modern power systems. Existing approaches often rely on deterministic or single-model forecasting and rarely integrate uncertainty-aware learning, exploratory data preprocessing, and multi-objective optimization within a unified structure. To address these limitations, this study develops a novel hybrid framework that combines Python-based exploratory data analysis (EDA) with MATLAB-driven modeling for the design and optimization of an Integrated Smart Energy System (ISES) to improve flexibility and mitigate peak demand. The Python stage enhances data quality through rigorous preprocessing such as outlier removal, missing-value imputation, normalization, and dimensionality reduction, ensuring data reliability under uncertainty. An Active Learning Artificial Neural Network (AL-ANN) is integrated within a multi-objective optimization framework to minimize forecast deviation, Levelized Cost of Energy (LCOE), and Loss of Power Supply Probability (LPSP). Comparative evaluations confirm that the proposed AL-ANN outperforms existing models, achieving higher forecasting accuracy, narrower prediction intervals, and lower emissions. Overall, the framework offers a robust, scalable, and uncertainty-resilient foundation for sustainable nextgeneration smart energy systems.