Foundation Models for Clean Energy Forecasting: A Comprehensive Review

Abstract

As global energy systems transit to clean energy, accurate renewable generation and renewable demand forecasting is imperative for effective grid management. Foundation Models (FMs) can help improve forecasting of renewable generation and demand because FMs can rapidly process complex, high-dimensional time-series data. This review paper focuses on FMs in the realm of renewable energy forecasting, primarily focusing on wind and solar. We present an overview of the architectures, pretraining strategies, finetuning methods, and types of data used in the context of renewable energy forecasting. We emphasize the role of models that are trained at a large scale, domain specific Transformer architectures, where attention is paid to spatial temporal correlations, the embedding of domain knowledge, and also the brief and intermittent nature of renewable generation. We assess recent FM based advancements in forecast accuracy such as reconciling predictions over multiple time scales and quantifying uncertainty in renewable energy forecasting. We also review existing challenges and areas of improvement in long-term and multivariate time series forecasting. In this survey, a distinction between theory and practice is established regarding the use of FMs in the clean energy forecasting domain. Additionally, it critically assesses the strengths and weaknesses of FMs while advancing future research direction in this new and exciting area of forecasting.