Related papers: Unified machine learning tasks and datasets for enhancing renewable energy

Unified machine learning tasks and datasets for enhancing renewable energy

URL: http://arxiv.org/abs/2311.06876v1
Date: Sun, 12 Nov 2023 15:30:44 GMT
Title: Unified machine learning tasks and datasets for enhancing renewable energy
Authors: Arsam Aryandoust, Thomas Rigoni, Francesco di Stefano, Anthony Patt
Abstract summary: We introduce the ETT-17 (Energy Transition Tasks-17), a collection of 17 datasets related to enhancing renewable energy. We unify all tasks and datasets, such that they can be solved using a single multi-tasking ML model.
Score: 0.8356833388425764
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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