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 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…
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,…
Autonomous mobile robots operating in compact or resource-constrained environments increasingly rely on visual perception for safe and efficient navigation. However, conventional vision-based algorithms often depend on computationally intensive neural networks that exceed the pro…
This paper proposes an expert recommendation algorithm based on the fusion of Transformer and Convolutional Neural Network (CNN) architectures, combined with a multi-task learning (MTL) framework, to improve the accuracy of expert recommendations in online question-answering comm…
To address cross-cultural pragmatic mismatches and insufficient consistency in interactive feedback within virtual reality language learning scenarios, this study constructs a framework for a cross-cultural virtual reality language learning system using immersive language learnin…
The rapid advancement of traditional machine learning has opened up new avenues within the medical field. For the automated screening of paediatric pneumonia via chest radiographs, this paper proposes a ‘deep features + heterogeneous ensemble’ framework. Utilising Kaggle’s datase…