Budget-constrained Collaborative Renewable Energy Forecasting Market

TL;DR

This paper introduces a market-based approach with incentive mechanisms for decentralized renewable energy forecasting, improving accuracy and fairness in data sharing among stakeholders.

Contribution

It proposes a bidding market and incentive mechanism for collaborative renewable energy forecasting, enhancing data sharing and forecast accuracy.

Findings

Over 10% RMSE improvement for wind power forecasts

Effective incentive mechanism prevents redundant feature use

Fair data compensation encourages stakeholder participation

Abstract

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.