Applying Machine Learning to Improve Load Forecasting and Scheduling Efficiency of Virtual Power Plants

As the penetration rate of distributed energy in virtual power plant (VPP) continues to rise, the load volatility and randomness of both the source and the load are significantly enhanced. Traditional statistical prediction models based on historical data are difficult to accurately describe the coupling characteristics under multiple time scales, resulting in a widening deviation between the scheduling plan and the real-time demand response. To this end, this study proposes a data-model-co-driven intelligent optimization framework. First, a multi-modal data fusion mechanism is constructed to integrate multi-dimensional heterogeneous data. Then, a hybrid prediction model based on CNN-BiGRU (Constitutional Neural Network-Bidirectional Gated Recurrent Unit) is designed, and a transfer learning strategy is introduced to solve the generalization bottleneck in small sample scenarios. Finally, this paper proposes an improved NSGA-III (Non-dominated Sorting Genetic Algorithm III) multi-objective optimization algorithm, combined with a rolling correction strategy to achieve closed-loop updates of minute-level scheduling instructions. Experiments show that the load forecasting model based on CNN-BiGRU improves the forecasting accuracy by about 26.3%–56.1% compared with other models in multiple time periods and extreme weather conditions. At the same time, the improved NSGA-III optimization algorithm shows high scheduling robustness and effectively reduces carbon emissions in extreme weather scenarios. In the above data conclusions, the proposed intelligent optimization framework can significantly improve the load forecasting and scheduling efficiency of virtual power plants, and provide technical support for the construction of green and low-carbon power systems.