Long Short-Term Memory Network and Graph Embedding to Analyze Distributed Photovoltaic Output Characteristics

Abstract

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.