Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts

TL;DR

This paper introduces an adaptive, explainable continual learning framework tailored for regression problems, specifically applied to power forecasts, addressing challenges like data non-stationarity and model adaptability in real-world scenarios.

Contribution

It extends the CLeaR framework to enable models to learn continuously and adaptively for regression tasks, incorporating dynamic hyperparameter updates and explainability features.

Findings

Effective power forecasting with continual learning demonstrated on real-world data

Framework adapts to non-stationary data streams and evolving data distributions

Visualization tools enhance understanding of model performance and data changes

Abstract

Compared with traditional deep learning techniques, continual learning enables deep neural networks to learn continually and adaptively. Deep neural networks have to learn new tasks and overcome forgetting the knowledge obtained from the old tasks as the amount of data keeps increasing in applications. In this article, two continual learning scenarios will be proposed to describe the potential challenges in this context. Besides, based on our previous work regarding the CLeaR framework, which is short for continual learning for regression tasks, the work will be further developed to enable models to extend themselves and learn data successively. Research topics are related but not limited to developing continual deep learning algorithms, strategies for non-stationarity detection in data streams, explainable and visualizable artificial intelligence, etc. Moreover, the framework- and algorithm-related hyperparameters should be dynamically updated in applications. Forecasting experiments will be conducted based on power generation and consumption data collected from real-world applications. A series of comprehensive evaluation metrics and visualization tools can help analyze the experimental results. The proposed framework is expected to be generally applied to other constantly changing scenarios.