Many industries rely on data-driven analytics, yet useful datasets are often distributed amongst market competitors that are reluctant to collaborate and share information. Recent literature proposes analytics markets to provide monetary incentives for data sharing, however many of these market designs are vulnerable to malicious forms of replication — whereby agents replicate their data and act under multiple identities to increase revenue. We develop a replication-robust analytics market, centering on supervised learning for regression. To allocate revenue, we use a Shapley value-based attribution policy, framing the features of agents as players and their interactions as a characteristic function game. We show that there are different ways to describe such a game, each with causal nuances that affect robustness to replication. Our proposal is validated using a real world wind power forecasting case study.

Energy management costs can be reduced by increasing load forecasting accuracy. Many studies use multiple data sources to potentially yield improved forecasts. However, data owners may be hesitant to share their data due to privacy concerns and a lack of monetary incentives. Here, we establish a model trading market to encourage collaboration, in which a buyer can purchase advanced load forecasting models that have been trained by sellers’ data to facilitate his decision-making. Specifically, we first propose a cost-oriented loss function that links to the buyer’s decision-making problem, which serves as a basis for market participants to evaluate the model quality and design their utility functions. Then, an iterative model auction mechanism is proposed to orchestrate selfish market participants to reach a consensus on the social welfare-maximizing solution, where the model quality and price for trading are determined through a distributed process. Furthermore, we propose a model adaptation strategy, including model fine-tuning and ensembling, for the buyer to enhance the applicability of purchased models to his decision-making problem. Experiments for building energy management are conducted based on public datasets. Results show that our approach converges to the socially optimal point and every participant can benefit from the market: sellers are compensated for providing models; and, the buyer can greatly reduce operational costs by employing the purchased models.

This paper studies how to aggregate prosumers (or large consumers) and their collective decisions in electricity markets, with a focus on fairness. Fairness is essential for prosumers to participate in aggregation schemes. Some prosumers may not be able to access the energy market directly, even though it would be beneficial for them. Therefore, new companies offer to aggregate them and promise to treat them fairly. This leads to a fair resource allocation problem.

We propose to use acceptability constraints to guarantee that each prosumer gains from the aggregation.
Moreover, we aim to distribute the costs and benefits fairly, taking into account the multi-period and uncertain nature of the problem. Rather than using financial mechanisms to adjust for fairness issues, we focus on various objectives and constraints, within decision problems, that achieve fairness by design. We start from a simple single-period and deterministic model, and then generalize it to a dynamic and stochastic setting using, e.g., stochastic dominance constraints.