Geographically distributed wind turbines, photovoltaic panels and sensors (e.g., pyranometers) produce large volumes of data that can be used to improve renewable energy sources (RES) forecasting skill. However, data owners may be unwilling to share their data, even if privacy is ensured, due to a form of prisoner's dilemma: all could benefit from data sharing, but in practice no one is willing to do do. Our proposal hence consists of a data marketplace, to incentivize collaboration between different data owners through the monetization of data. We adapt here an existing auction mechanism to the case of RES forecasting data. It accommodates the temporal nature of the data, i.e., lagged time-series act as covariates and models are updated continuously using a sliding window. A test case with wind energy data is presented to illustrate and assess the effectiveness of such data markets. All agents (or data owners) are shown to benefit in terms of higher revenue resulting from the combination of electricity and data markets. The results support the idea that data markets can be a viable solution to promote data exchange between RES agents and contribute to reducing system imbalance costs.

中文翻译：

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走向可再生能源预测中的数据市场

地理分布的风力涡轮机，光伏面板和传感器（例如，日射强度计）产生大量数据，可用于提高可再生能源（RES）的预测技能。但是，由于囚徒的困境，即使确保了隐私，数据所有者也可能不愿共享其数据：所有人都可以从数据共享中受益，但实际上没有人愿意这样做。因此，我们的建议包括一个数据市场，以通过数据货币化激励不同数据所有者之间的协作。在这里，我们将现有的拍卖机制适应RES预测数据的情况。它适应了数据的时间特性，即，滞后的时间序列充当协变量，并且使用滑动窗口连续更新模型。提出了一个带有风能数据的测试案例，以说明和评估此类数据市场的有效性。从电力和数据市场的结合中可以看出，所有代理商（或数据所有者）都将从更高的收益中受益。结果表明，数据市场可以成为促进RES代理之间的数据交换并有助于降低系统不平衡成本的可行解决方案。