The volatility of power grid loads and the uncertainty of distributed energy resources pose challenges to operational economy and safety. Flexible load resource regulation is key to mitigating fluctuations and improving energy efficiency, yet traditional optimization methods have limitations in complex spatiotemporal correlations and real-time responsiveness. This study proposes an integrated deep learning optimization framework incorporating multiple algorithms, designed with a system solution featuring prediction, decision-making, and coordination modules. The method employs CNN-LSTM for precise load forecasting, utilizes deep reinforcement learning to establish real-time regulation strategies, and leverages graph neural networks for multi-load coordination. Experimental validation demonstrates that the proposed approach outperforms conventional methods across multiple performance metrics, providing an effective solution for intelligent operation of industrial park power grids.
In the context of interdisciplinary integration between advanced manufacturing and artificial intelligence, the optimization of complex part machining parameters has become a key challenge in intelligent production systems. Combining mechanical engineering, data science, and cont…
Dynamic scheduling in intelligent logistics distribution systems involves high-dimensional state representation, stochastic order arrivals, and complex route constraints, which make traditional scheduling methods less effective in real-time environments. To address this problem,…
In the wave of intelligent manufacturing transformation, production workshops are facing core challenges such as relying on manual quality inspection, lagging equipment failure prediction, and rigid production scheduling. Deep learning technology, with its powerful perception and…
With the evolution toward 5G-Advanced and 6G autonomous communication networks, achieving energy-efficient closed-loop operations and maintenance (O&M) has become increasingly challenging due to large-scale deployment, heterogeneous network elements, and highly dynamic traffi…
Accurate and efficient quality inspection and acceptance are essential for ensuring construction performance and reducing rework in building projects; however, conventional inspection methods are labor-intensive, subjective, and difficult to scale in complex construction environm…
Artificial intelligence-driven deep point cloud learning technology offers novel solutions for low-altitude surveying and mapping 3D reconstruction. Addressing the accuracy limitations of traditional reconstruction algorithms under conditions of sparse point clouds, pose drift, a…