Unified machine learning tasks and datasets for enhancing renewable
  energy
Arsam Aryandoust Thomas Rigoni Francesco di Stefano Anthony Patt
Multi-tasking machine learning (ML) models exhibit prediction abilities in
domains with little to no training data available (few-shot and zero-shot
learning). Over-parameterized ML models are further capable of zero-loss
training and near-optimal generalization performance. An open research question
is, how these novel paradigms contribute to solving tasks related to enhancing
the renewable energy transition and mitigating climate change. A collection of
unified ML tasks and datasets from this domain can largely facilitate the
development and empirical testing of such models, but is currently missing.
Here, we introduce the ETT-17 (Energy Transition Tasks-17), a collection of 17
datasets from six different application domains related to enhancing renewable
energy, including out-of-distribution validation and testing data. We unify all
tasks and datasets, such that they can be solved using a single multi-tasking
ML model. We further analyse the dimensions of each dataset; investigate what
they require for designing over-parameterized models; introduce a set of
dataset scores that describe important properties of each task and dataset; and
provide performance benchmarks.