# A Collection and Categorization of Open-Source Wind and Wind Power Datasets

arXiv: https://arxiv.org/abs/2202.08524 · HTML: https://mlsanity.com/paper/2202.08524/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 99 references that are in the map.
- First posted on arXiv 17 Feb 2022.

## Builds on (most cited first, 3 of 3)

- [Monash Time Series Forecasting Archive](https://mlsanity.com/paper/2105.06643/) (2021)
- [Model-Free Renewable Scenario Generation Using Generative Adversarial Networks](https://mlsanity.com/paper/1707.09676/) (2017)
- [FIRST: A Framework for Optimizing Information Quality in Mobile Crowdsensing Systems](https://mlsanity.com/paper/1804.11147/) (2018)

## Built on by (most cited first, 5 of 5)

- [SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting Challenge at KDD Cup 2022](https://mlsanity.com/paper/2208.04360/) (2022)
- [Prediction of wind turbines power with physics-informed neural networks and evidential uncertainty quantification](https://mlsanity.com/paper/2307.14675/) (2023)
- [WPBench: A Comprehensive Benchmark for Wind Power Forecasting](https://mlsanity.com/paper/2609.24444/) (2026)
- [CARE to Compare: A real-world dataset for anomaly detection in wind turbine data](https://mlsanity.com/paper/2404.10320/) (2024)
- [Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6](https://mlsanity.com/paper/2408.14889/) (2024)

## Abstract (arXiv)

Wind power and other forms of renewable energy sources play an ever more important role in the energy supply of today's power grids. Forecasting renewable energy sources has therefore become essential in balancing the power grid. While a lot of focus is placed on new forecasting methods, little attention is given on how to compare, reproduce and transfer the methods to other use cases and data. One reason for this lack of attention is the limited availability of open-source datasets, as many currently used datasets are non-disclosed and make reproducibility of research impossible. This unavailability of open-source datasets is especially prevalent in commercially interesting fields such as wind power forecasting. However, with this paper we want to enable researchers to compare their methods on publicly available datasets by providing the, to our knowledge, largest up-to-date overview of existing open-source wind power datasets, and a categorization into different groups of datasets that can be used for wind power forecasting. We show that there are publicly available datasets sufficient for wind power forecasting tasks and discuss the different data groups properties to enable researchers to choose appropriate open-source datasets and compare their methods on them.