The expansion of renewable energy sources in the residential sector, while an important step towards a more sustainable energy landscape, poses unique challenges for power system operators. The intermittent nature of renewable energy generation, primarily from solar and wind, introduces significant variability and uncertainty into the grid. Unlike conventional power plants, which provide steady and controllable output, renewable generation depends heavily on weather conditions, leading to rapid and sometimes unpredictable fluctuations in supply. To mitigate these challenges, system operators must invest in advanced grid management systems, flexible demand-response mechanisms, and large-scale energy storage solutions. Additionally, accurate forecasting of net load—defined as the total electricity demand minus renewable generation—is crucial for maintaining grid stability, optimizing energy dispatch, and reducing reliance on costly balancing reserves. Traditional forecasting methods often struggle to capture the nonlinear and stochastic nature of net load, particularly in systems with high renewable penetration. This paper presents a comprehensive framework for day-ahead net load forecasting, leveraging advanced machine learning techniques to improve prediction accuracy. The proposed VMD-MIIG-CNN-GRU-BiLSTM approach effectively captures both spatial and temporal dependencies in net load data while accounting for external factors such as weather patterns and consumption behavior. Experimental results demonstrate that the proposed model achieves a MAPE of approximately 8%, significantly outperforming conventional forecasting methods. The framework’s robustness in handling the inherent complexities of renewable-integrated power systems makes it a valuable tool for grid operators, enabling more efficient resource allocation and enhanced system reliability.