An Accelerated Continual Learning with Demand Prediction based Scheduling in Edge-Cloud Computing

Abstract

As the development of smart grid with Advanced Metering Infrastructure (AMI) consisting of network infrastructure, smart meter, and data management system, the smart grid system can analyze energy data to efficiently control energy generation and distribution. Through recent advance of analysis based on neural network, some deep neural networks have proven to perform better than conventional analytical techniques. However, Basic learning process is facing challenges on analyze time-series data from smart meter based on deep learning in realtime. Although the strategies of gradually learning a deep neural network through the continual learning method was proposed, it is only effective when data feature is not significantly changed, therefore, the performance improvements are still needed on environment where the data distribution fluctuates according to different power consumption habits. Therefore, we proposed a scheduled continual deep learning on edge-cloud system to improve and accelerate learning performance on the multi-client power consumption data, which biased data feature varies dramatically. Using cosine similarity of electric load pattern, the scheduling algorithm manages and controls the gradient from optimizing process. The evaluated performance with general experiments shows the validity of proposed scheme compared to the base method.