This work tackles the scheduling challenge of microgrids for smart homes, aiming to optimize energy management with both renewable and non-renewable sources. A power control center orchestrates the microgrid, coordinating distributed energy resources (DERs) for peak demand fulfillment and excess energy utilization. We propose a proportional-integral control system for efficient demand response, achieving reduced post-scheduling costs and a peak-to-average ratio. Comparative analysis reveals Ant Colony Optimization outperforms Binary Particle Swarm Optimization in cost and peak-to-average ratio reduction. Simulations explore two scenarios: Case 1 integrates with the main grid for reliability, while Case 2 utilizes solely renewable energy sources. Although Case 2 exhibits superior performance, Case 1’s dependence on the main grid offers greater real-world feasibility. Therefore, Case 1 with optimized DER scheduling emerges as the recommended solution for enhancing microgrid efficiency and ensuring reliable power supply in smart homes.

COVID-19 is a profoundly contagious pandemic that has taken the world by storm and has afflicted different fields of life with negative effects. It has had a substantial impact on education which is evident from the transition from Face-to-Face (F2F) teaching to online mode of education and the rigid implementation of lockdown across the globe. This study examines the factors impacting the performance of teachers during the lockdown period of COVID-19 using various feature selection algorithms and Artificial Intelligence techniques. In this paper, we have proposed a novel multilevel feature selection for the prediction of the factors affecting the teachers’ satisfaction with online teaching and learning in COVID-19. The proposed multilevel feature selection is composed of the Fast Correlation Based Filter (FCBF), Mutual Information (MI), Relieff, and Particle Swarm Optimization (PSO) feature selection. The performance of the proposed feature selection approach is evaluated through the teachers’ benchmark dataset. We used a range of measures like accuracy, precision, f-measure, and recall to evaluate the performance of the proposed approach. We applied different machine learning approaches (SVM, LGBM, and ANN) with the proposed multilevel feature selection technique. The performance of the proposed approach is also compared with existing feature selection algorithms, and the results show the improvement in the performance of feature selection in terms of accuracy, precision, recall, and F-Measure. Proposed feature selection provides more than 80% accuracy with Light Weight Gradient Boosting Machine (LGBM).