# Budget-constrained Collaborative Renewable Energy Forecasting Market

arXiv: https://arxiv.org/abs/2501.12367 · HTML: https://mlsanity.com/paper/2501.12367/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 38 references that are in the map.
- First posted on arXiv 21 Jan 2025.

## Builds on (most cited first, 7 of 7)

- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Next Generation Reservoir Computing](https://mlsanity.com/paper/2106.07688/) (2021)
- [A Marketplace for Data: An Algorithmic Solution](https://mlsanity.com/paper/1805.08125/) (2018)
- [Regression markets and application to energy forecasting](https://mlsanity.com/paper/2110.03633/) (2021)
- [Optimized, Direct Sale of Privacy in Personal-Data Marketplaces](https://mlsanity.com/paper/1701.00740/) (2017)
- [Trading Data for Wind Power Forecasting: A Regression Market with Lasso Regularization](https://mlsanity.com/paper/2110.07432/) (2021)
- [Bayesian Regression Markets](https://mlsanity.com/paper/2310.14992/) (2023)

## Abstract (arXiv)

Accurate power forecasting from renewable energy sources (RES) is crucial for integrating additional RES capacity into the power system and realizing sustainability goals. This work emphasizes the importance of integrating decentralized spatio-temporal data into forecasting models. However, decentralized data ownership presents a critical obstacle to the success of such spatio-temporal models, and incentive mechanisms to foster data-sharing need to be considered. The main contributions are a) a comparative analysis of the forecasting models, advocating for efficient and interpretable spline LASSO regression models, and b) a bidding mechanism within the data/analytics market to ensure fair compensation for data providers and enable both buyers and sellers to express their data price requirements. Furthermore, an incentive mechanism for time series forecasting is proposed, effectively incorporating price constraints and preventing redundant feature allocation. Results show significant accuracy improvements and potential monetary gains for data sellers. For wind power data, an average root mean squared error improvement of over 10% was achieved by comparing forecasts generated by the proposal with locally generated ones.