In many areas of industry and society, including energy, healthcare, and logistics, agents collect vast amounts of data that are deemed proprietary. These data owners extract predictive information of varying quality and relevance from data depending on quantity, inherent information content, and their own technical expertise. Aggregating these data and heterogeneous predictive skills, which are distributed in terms of ownership, can result in a higher collective value for a prediction task. In this paper, a platform for improving predictions via the implicit pooling of private information in return for possible remuneration is envisioned. Specifically, a wagering-based forecast elicitation market platform has been designed, in which a buyer intending to improve their forecasts posts a prediction task, and sellers respond to it with their forecast reports and wagers. This market delivers an aggregated forecast to the buyer (pre-event) and allocates a payoff to the sellers (post-event) for their contribution. A payoff mechanism is proposed and it is proven that it satisfies several desirable economic properties, including those specific to electronic platforms. Furthermore, the properties of the forecast aggregation operator and scoring rules are discussed in order to emphasize their effect on the sellers’ payoff. Finally, numerical examples are provided in order to illustrate the structure and properties of the proposed market platform.

Accurate short-term power forecasts are crucial for the reliable and efficient integration of wind energy in power systems and electricity markets. Typically, forecasts for hours to days ahead are based on the output of numerical weather prediction models, and with the advance of computing power, the spatial and temporal resolutions of these models have increased substantially. However, high-resolution forecasts often exhibit spatial and/or temporal displacement errors, and when regarding typical average performance metrics, they often perform worse than smoother forecasts from lower-resolution models. Recent computational advances have enabled the use of large-eddy simulations (LESs) in the context of operational weather forecasting, yielding turbulence-resolving weather forecasts with a spatial resolution of 100 m or finer and a temporal resolution of 30 seconds or less. This paper is a proof-of-concept study on the prospect of leveraging these ultra high-resolution weather models for operational forecasting at Horns Rev I in Denmark. It is shown that temporal smoothing of the forecasts clearly improves their skill, even for the benchmark resolution forecast, although potentially valuable high-frequency information is lost. Therefore, a statistical post-processing approach is explored on the basis of smoothing and feature engineering from the high-frequency signal. The results indicate that for wind farm forecasting, using information content from both the standard and LES resolution models improves the forecast accuracy, especially with a feature selection stage, compared with using the information content solely from either source.