Continuous Model Adaptation Using Online Meta-Learning for Smart Grid Application

Jinghang Li, Mengqi Hu

2020IEEE Transactions on Neural Networks and Learning Systems25 citationsDOI

Abstract

The rapid development of deep learning algorithms provides us an opportunity to better understand the complexity in engineering systems, such as the smart grid. Most of the existing data-driven predictive models are trained using historical data and fixed during the execution stage, which cannot adapt well to real-time data. In this research, we propose a novel online meta-learning (OML) algorithm to continuously adapt pretrained base-learner through efficiently digesting real-time data to adaptively control the base-learner parameters using meta-optimizer. The simulation results show that: 1) both ML and OML can perform significantly better than online base learning. 2) OML can perform better than ML and online base learning when the training data are limited, or the training and real-time data have very different time-variant patterns.

Topics & Concepts

Computer scienceMeta learning (computer science)Base (topology)Adaptation (eye)GridMachine learningOnline learningArtificial intelligenceOnline machine learningTraining setData miningActive learning (machine learning)MultimediaEngineeringGeometryPhysicsMathematical analysisSystems engineeringMathematicsOpticsTask (project management)Energy Load and Power ForecastingMachine Learning and ELMDomain Adaptation and Few-Shot Learning