# Trading Data for Wind Power Forecasting: A Regression Market with Lasso Regularization

arXiv: https://arxiv.org/abs/2110.07432 · HTML: https://mlsanity.com/paper/2110.07432/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2021.

## Built on by (most cited first, 3 of 3)

- [Bayesian Regression Markets](https://mlsanity.com/paper/2310.14992/) (2023)
- [How to Purchase Labels? A Cost-Effective Approach Using Active Learning Markets](https://mlsanity.com/paper/2511.20605/) (2025)
- [Budget-constrained Collaborative Renewable Energy Forecasting Market](https://mlsanity.com/paper/2501.12367/) (2025)

## Abstract (arXiv)

This paper proposes a regression market for wind agents to monetize data traded among themselves for wind power forecasting. Existing literature on data markets often treats data disclosure as a binary choice or modulates the data quality based on the mismatch between the offer and bid prices. As a result, the market disadvantages either the data sellers due to the overestimation of their willingness to disclose data, or the data buyers due to the lack of useful data being provided. Our proposed regression market determines the data payment based on the least absolute shrinkage and selection operator (lasso), which not only provides the data buyer with a means for selecting useful features, but also enables each data seller to individualize the threshold for data payment. Using both synthetic data and real-world wind data, the case studies demonstrate a reduction in the overall losses for wind agents who buy data, as well as additional financial benefits to those who sell data.