Data-driven forecasting has become increasingly important for describing the temporal interactions among heterogeneous variables in modern power systems. To capture the nonlinear coupling, heterogeneous fluctuations, and multi-scale temporal variations between renewable generation and load demand, this study develops an MT-Transformer framework for coordinated wind–solar–load forecasting. Meteorological variables, historical renewable output, historical load, and temporal labels are integrated as model inputs. A shared Transformer encoder is used to learn common temporal representations, and task-specific forecasting heads are designed to generate synchronized predictions for wind power, PV power, and load. The experimental results show that MT-Transformer achieves an MAE of 0.0848, an RMSE of 0.1254, and an R² of 0.9302. Compared with the Persistence Model, the MAE and RMSE decrease by 36.05% and 30.49%, respectively. The predicted outputs are further converted into a net-load sequence, from which fluctuation indicators are derived. The peak–valley difference reaches 0.462 p.u., and the maximum ramp rate reaches 0.087 p.u./h, indicating evident peak-shaving pressure and short-term regulation demand. These findings confirm that the proposed framework improves coordinated forecasting performance and provides quantitative evidence for coal-power peak regulation, reserve capacity allocation, and ancillary service demand identification.
Meng Huang, Lei Wang, Teng Luo et al.· EAI Endorsed Transactions on...· 0 citations
Accurate prediction of distributed photovoltaic (PV) output is essential for modern smart grids and electromagnetic energy infrastructure, where renewable generation exhibits strong nonlinearity and long-term temporal dependence due to cloud occlusion and meteorological variations. Traditional forecasting methods often struggle to characterize cross-node interactions and accumulate prediction errors under complex operating conditions. To address these issues, this paper proposes a hybrid spatiotemporal prediction framework integrating an improved Long Short-Term Memory (LSTM) network with dynamic graph embedding for deep feature mining and coordinated forecasting. A multi-layer residual LSTM with adaptive attention first models long-term dependencies and transient fluctuations from preprocessed time series data. A dynamic graph is then constructed according to feeder connectivity and geographical proximity, where Dynamic GraphSAGE generates node embeddings to capture evolving spatial relationships. Temporal features and graph representations are fused through a Graph Attention Network (GAT) and multi-layer LSTM to jointly model spatiotemporal interactions, while TimeGAN-based sparse data completion and Bayesian optimization further enhance robustness and parameter adaptation. Experimental results demonstrate RMSE values of 0.10, 0.12, and 0.13 for 30 min, 3 h, and 6 h forecasting horizons, respectively, with training speed 20% faster than GCN-LSTM, only a 20% RMSE increase under σ = 0.1 noise, and RMSE remaining below 0.14 at unseen sites. The proposed framework provides an effective solution for distributed PV forecasting and offers methodological support for intelligent electromagnetic energy management and resilient smart power systems.
S. Wan, J. Tan, T. Luo et al.· Advanced Electromagnetics· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
