M. Zhang

#reinforcement learning Open access Aug 2026

Research on Accurate Load Forecasting and Optimization Algorithm for Distribution Networks Based on Improved BiLSTM and DRL (High Load Density Areas)

An integrated algorithm based on improved Bidirectional Long Short-Term Memory and Deep Reinforcement Learning provides an effective solution for intelligent load prediction and adaptive energy management, offering practical support for electromagnetic energy distribution and resilient operation in next-generation smart power systems.

Y.-F. Zeng, Y.-Y. Wang, M.-K. Li et al. · Advanced Electromagnetics · 0 citations

Open access Aug 2026

Power Spot Market Supply and Demand Forecasting and Indicator Anomaly Detection Method Integrating Autoformer and Bayesian Optimization

Accurate forecasting of electricity spot market supply and demand and timely anomaly detection are essential for intelligent energy management and communication-assisted monitoring systems operating over distributed electromagnetic information networks. To improve prediction accuracy under high-frequency non-stationary fluctuations a nd a ccelerate h yperparameter c onvergence i n multi-source feature spaces, this study proposes a power market forecasting and indicator anomaly detection framework integrating Autoformer with particle swarm optimization and Bayesian optimization (PSO-BO). The Autoformer model exploits trend–seasonal decomposition to capture long- and short-term temporal dependencies, while PSO performs global exploration and Bayesian optimization refines local hyperparameters for efficient model tuning. A Bayesian probabilistic anomaly detection model based on a normal-inverse-gamma prior is further established to quantify residual uncertainty and achieve adaptive online warning. Experimental results on a 24-hour rolling forecasting task demonstrate MAE/RMSE values of 1.23/1.75 GW for load prediction and 1.85/2.53 GW for generation prediction, with anomaly detection accuracy reaching 93.87%. The proposed framework exhibits strong sensitivity to rapid market fluctuations and provides an effective solution for realtime intelligent scheduling and secure monitoring. Moreover, its distributed forecasting and adaptive decision mechanisms offer valuable support for communication-enabled energy systems and electromagnetic sensing infrastructures requiring reliable information transmission and operational awareness.

Y. Ma, J. Cui, M. Zhang et al. · Advanced Electromagnetics · 0 citations