Budget-constrained Collaborative Renewable Energy Forecasting Market
This addresses the challenge of integrating decentralized spatio-temporal data for renewable energy forecasting, which is incremental as it builds on existing forecasting and market mechanisms.
The paper tackles the problem of decentralized data ownership hindering renewable energy forecasting by proposing a bidding mechanism for data sharing and advocating spline LASSO regression models, achieving over 10% improvement in wind power forecasting accuracy.
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.