A distribution system’s network reconfiguration (NR) is the process of changing the status of the switches to change the topology of the feeders. Using optimal NR at various network load levels, this research proposed an economical way for improving the voltage profile and reducing power loss in distribution systems. A new metaheuristic Heap-based optimizer based on the corporate rank hierarchy is used to solve the optimization problem in order to find the optimal radial distribution network. The objective functions used in this article include voltage profile improvement, power loss reduction, reduction of operation cost, and minimization of gas emissions. This novel technique provides accurate results while avoiding the issue of premature convergence and requires fewer control parameter settings. The proposed method is evaluated at three different load levels on a standard IEEE 33-bus test system, and the simulation results are compared to those of other optimization methods published in the literature, demonstrating that the proposed method improves the system voltage profile while minimizing losses and power costs, establishing that this technique is effective in finding the best result. The Wilcoxon and Friedman tests are also used to verify the effectiveness of the proposed technique.

Accurate short-term forecasting of photovoltaic (PV) output is critical to managing the variability of PV generation and ensuring reliable grid operation with high renewable integration. We propose an enhanced hybrid deep learning framework that combines Temporal Convolutional Networks (TCNs), Gated Recurrent Units (GRUs), and Random Forests (RFs) in an optimized weighted ensemble strategy. This approach leverages the complementary strengths of each component: TCNs capture long-range temporal dependencies via dilated causal convolutions; GRUs model sequential weather-driven dynamics; and RFs enhance robustness to outliers and nonlinear relationships. The model was evaluated on high-resolution operational data from the Yulara solar plant in Australia, forecasting horizons from 5 min to 1 h. Results show that the TCN-GRU-RF model consistently outperforms conventional benchmarks, achieving R² = 0.9807 (MAE = 0.0136; RMSE = 0.0300) at 5 min and R² = 0.9047 (RMSE = 0.0652) at 1 h horizons. Notably, the degradation in R² across forecasting horizons was limited to 7.7%, significantly lower than the typical 10–15% range observed in the literature, highlighting the model’s scalability and resilience. These validated results indicate that the proposed approach provides a robust, scalable forecasting solution that enhances grid reliability and supports the integration of distributed renewable energy sources.