Joint Forecasting Method for Wind and Solar Power Generation and Electricity Load

In today's energy field, Wind Turbine Generator (WTG) and photovoltaic power (PV) have become the mainstream forms of wind and solar energy development and utilization. They play a key role in the global energy transition process with their clean and renewable advantages. However, the output power of wind and PV generation exhibits significant volatility and instability. This characteristic makes it difficult to accurately control and schedule in practical operation, posing many challenges to the grid connected operation of the power grid. Based on this, this paper proposes a deep learning (DL) based joint prediction model for wind and solar power (WSP) generation and electricity load. This model improves the prediction accuracy of WSP generation and load by deeply exploring and learning the dynamic coupling relationship between them. After extensive experimental verification, the model constructed in this article can accurately predict the power and load of WSP generation, providing strong data support for the stable operation and reasonable scheduling of the power grid, and helping to improve the safety and economy of the electrical power system (EPS).