Intelligent solar energy forecasting for smart grids using KARN–EWOA within 6G-enabled IoT frameworks

Abstract

Rapid urbanization and rising energy demands make sustainable energy management essential for future smart cities. This study aims to improve solar energy forecasting and smart grid efficiency by tackling issues like data inconsistencies, model optimization, and prediction accuracy. The proposed solution, titled 'Advancing Sustainable Urban Development through Intelligent Energy Management in Smart Grids,' leverages 6G IoT and Artificial Intelligence to enhance energy systems in Next-Generation Smart Cities (EMSG-KARN). This study aims to contribute to the Sustainable Development Goal (SDG) for energy, aligning with the national priority area of energy. Input data from Solar Power Generation and Weather Sensor datasets undergo pre-processing using the Adaptive Resilience Navigation Filter for cleaning and normalization. The processed data is then fed into the Kolmogorov–Arnold Recurrent Network (KARN) to predict solar energy production. Results show that EMSG-KARN achieves superior predictive performance, with 98% accuracy, 97% precision, 98.5% recall, and an F1-score of 96.5%, indicating significant improvements over traditional models. Ablation and cross-validation analyses confirm the contribution of each module, ensuring reliable and scalable solar energy forecasting. This framework provides urban energy planners with a robust, high-performing solution for integrating renewable energy sources, optimizing energy distribution, and improving grid efficiency. The proposed method supports sustainable urban development, climate goals, and offers a scalable, reliable approach for intelligent energy management in smart cities, bridging gaps left by existing AI-based techniques.