Budget-constrained Collaborative Renewable Energy Forecasting Market
Budget-constrained Collaborative Renewable Energy Forecasting Market
风能、风力机械独立电源技术
Carla Goncalves,Tiago Teixeira,Ricardo J. Bessa,Joao Vinagre.Budget-constrained Collaborative Renewable Energy Forecasting Market[EB/OL].(2025-01-21)[2025-09-24].https://arxiv.org/abs/2501.12367.点此复制
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.
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