提出数据市场机制，让风电数据共享更公平高效。
Budget-constrained Collaborative Renewable Energy Forecasting Market
- 用样条LASSO回归提升预测精度与可解释性。
- 风力发电预测误差降低超10%，数据提供方获额外收益。
- 适合关注能源数据共享与激励机制的研究者。
可再生能源（RES）的精准电力预测对电网集成和可持续发展目标至关重要。本文强调将分散的时空数据整合进预测模型的重要性，但数据所有权分散成为关键障碍，需设计激励机制促进数据共享。主要贡献包括：(a) 对比分析多种预测模型，推荐高效且可解释的样条LASSO回归；(b) 在数据/分析市场中引入竞价机制，确保数据提供方获得合理补偿，并允许买卖双方表达价格需求。此外，提出一种时间序列预测的激励机制，有效结合价格约束并避免冗余特征分配。实验结果表明，相比本地生成的预测，本方法在风力发电数据上平均均方根误差降低超过10%，同时为数据提供方带来潜在经济收益。
原文摘要 · Abstract (English)
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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