Reliably forecasting uncertain power production is beneficial for the social welfare of electricity markets by reducing the need for balancing resources. Describing such forecasting as an analytics task, the current literature proposes analytics markets as an incentive for data sharing to improve accuracy, for instance by leveraging spatio-temporal correlations. The challenge is that, when used as input features for forecasting, correlated data complicates the market design with respect to the revenue allocation, as the value of overlapping information is inherently combinatorial. We develop a correlation-aware analytics market for a wind power forecasting application. To allocate revenue, we adopt a Shapley value-based attribution policy, framing the features of agents as players and their interactions as a characteristic function game. We illustrate that there are multiple options to describe such a game, each having causal nuances that influence market behavior when features are correlated. We argue that no option is correct in a general sense, but that the decision hinges on whether the market should address correlations from a data-centric or model-centric perspective, a choice that can yield counter-intuitive allocations if not considered carefully by the market designer.

翻译：可靠地预测不确定的发电量对电力市场的社会福利具有积极意义，因为它可以减少对平衡资源的需求。将此类预测描述为一项分析任务，现有文献提出将分析市场作为激励数据共享以提升准确性的手段，例如通过利用时空相关性。问题在于，当相关数据被用作预测的输入特征时，会因重叠信息的价值具有固有权衡性而复杂化市场设计中的收益分配。我们为风电功率预测应用开发了一种考虑相关性的分析市场。为分配收益，我们采用基于沙普利值的归属策略，将智能体的特征视为博弈参与者，其交互关系表述为特征函数博弈。我们阐述描述此类博弈存在多种选择，每种选择在特征相关时均会影响市场行为，且具有因果层面的细微差异。我们认为没有一种选择在普遍意义上正确，但决策取决于市场应从数据中心视角还是模型中心视角处理相关性——若市场设计师未审慎考量这一选择，则可能产生有悖直觉的分配结果。