Dynamic Load Combined Prediction Framework with Collaborative Cloud-Edge for Microgrid

Electric load forecasting has emerged as a critical enabler of decision-making and scheduling for smart grids. However, most of the existing deep learning electricity prediction methods are trained offline in the cloud, which causes network congestion and long latency. Edge computing has shown great potential in training models at the network edge to ensure real-time. In this paper, we propose a dynamic combined prediction framework based on sparse anomaly sensing with cloud-edge collaboration to exploit the real-time characteristic of online prediction models on edge and the strong predictive ability of offline prediction models on the cloud. The proposed framework can reasonably process abnormal data by incorporating a sparse anomaly sensing approach, thus further improving the model prediction capability. For this demo, we develop an edge computing-based microgrid platform on which we have implemented a dynamic combined prediction scheme based on sparse anomaly sensing. Experimental results verify the practicability and feasibility performance of the proposed scheme.