Cloud-edge Collaborative Forecasting for Industrial Photovoltaic-storage-charging SystemsOA

With the growing integration of renewable energy into modern power grids,photovoltaic(PV)power has emerged as a pivotal element that is frequently coupled with energy-storage systems to enhance dispatch efficiency.However,the inherent variability in PV power generation and user load present significant challenges for accurate system scheduling.A key difficulty is that the PV output is often subject to artificial power curtailment,creating a non-linear relationship with solar irradiance,whereas charging loads exhibit distinct patterns between weekdays and weekends.To address this problem,a cloud-edge collaborative prediction system is proposed to implemented in a local energy management system(EMS).This system adopts a meta-learning-based multihead regression model that integrates XGBoost,support vector regression,random forest,Cat Boost,and a multilayer perceptron.For PV power prediction,historical rolling lag features are introduced to capture the curtailment patterns.For load prediction,time-based features are designed to reflect the behavioral habits of electricity consumption over time.The system is successfully deployed at a charging station in the Haier Industrial Park.A practically validated solution is demonstrated for addressing uncertainties in integrated energy systems,along with the effectiveness of edge-deployed algorithms in optimizing storage strategies and reducing operational costs.