Research on optimization of power grid load forecasting models based on deep learning

Precise forecasting of power grid load is essential for maintaining the stability and efficiency of contemporary energy systems. Traditional statistical and machine learning methods often struggle to capture the nonlinear temporal dependencies and dynamic variations in power loads, limiting their predictive accuracy. To overcome these difficulties, this study proposes an Adaptive Deep Load Predictor (ADLP) that integrates hybrid temporal encoding, a dynamic attention mechanism, and an online adaptive updating strategy. By integrating bidirectional long short-term memory networks for temporal sequence analysis with convolutional neural networks for extracting spatial features, ADLP effectively captures both short-term fluctuations and long-term dependencies in load patterns. The dynamic attention mechanism further enhances predictive performance by assigning adaptive importance to historical data, while the online adaptive updating module ensures real-time adaptability to evolving load conditions. This study introduces a Dynamic Grid-Aware Load Optimization (DGLO) strategy to enhance power grid operations based on real-time predictive insights. It integrates predictive adaptive control, stochastic robust optimization, and energy storage integration to dynamically adjust grid operations. The predictive adaptive control mechanism refines real-time generation adjustments using deep learning-based forecasting and proportional-integral-derivative control. The stochastic robust optimization component compensates for uncertainties in load variations, improving system resilience. The energy storage integration module optimizes charge-discharge cycles to stabilize energy utilization. Extensive experiments on real-world datasets demonstrate the superiority of the proposed framework over traditional and existing deep learning methods. The results confirm its enhanced forecasting precision, adaptability, and robustness in smart grid applications. Future work will focus on improving computational efficiency, integrating reinforcement learning, and expanding generalization across different grid environments.