Author

Wenchao Guo

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Open access Aug 2026

An MT-Transformer Framework for Coordinated Wind-Solar-Load Forecasting with Net-Load-Based Coal-Power Regulation Demand Identification

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

Conference Jul 2026

A CNN-LSTM-PINN Photovoltaic Power Forecasting Method Based on Data–Mechanism Fusion

With the increasing penetration of photovoltaic power in power systems, accurate photovoltaic power forecasting is important for dispatch optimization, and renewable energy accommodation. To address the problem that data-driven models may ignore photovoltaic generation mechanisms and produce physically inconsistent forecasting results, this paper proposes a CNN-LSTM-PINN photovoltaic power forecasting method based on data–mechanism fusion. The proposed method introduces physics-informed neural networks into a CNN-LSTM temporal forecasting structure, transforms the physical relationship among photovoltaic power, irradiance, and temperature into differentiable constraints, and constructs physical constraint losses based on partial-derivative direction relationships and power boundary conditions, enabling the model to fit historical data while satisfying photovoltaic generation mechanisms. Experimental results show that the proposed method achieves RMSE, MAE, and R2 values of 1.0583, 0.5727, and 0.8912 on the photovoltaic plant dataset, respectively, outperforming PINN CNN-LSTM, LSTM, and traditional machine learning models. Under a 0.30 noise pertur-bation level, its RMSE increases by only 2.86%, demonstrating good stability and anti-disturbance capability.

Qian Lei, Yongxu Chen, Qingyang Li et al. · 2026 5th International Confe... · 0 citations