Despite widespread adoption of machine learning, many firms face the common challenge of relevant datasets being distributed amongst market competitors whom are reluctant to share information. Accordingly, recent works propose analytics markets as a way to provide monetary incentives for collaboration, where agents share features and are rewarded based on their contribution to improving the predictions of others. These contributions are determined by their relative Shapley value, computed by treating features as players and their interactions as a cooperative game. However, this setup is known to incite agents to strategically replicate their data and act under multiple false identities to increase their own revenue whilst diminishing that of others, which limits the viability of these markets in practice.
In this work, we develop an analytics market robust to such strategic replication for supervised learning problems. We adopt Pearl’s do-calculus from causal inference to refine the cooperative game by differentiating between observational and interventional conditional probabilities. As a result, we derive Shapley value-based rewards that deter replication by design.

The growing uncertainty from renewable power and electricity demand brings significant challenges to unit commitment (UC). While various advanced forecasting and optimization methods have been developed to predict better and address this uncertainty, most previous studies treat forecasting and optimization as separate tasks. This separation can lead to suboptimal results due to misalignment between the objectives of the two tasks. To overcome this challenge, we propose a robust UC framework that integrates the forecasting and optimization processes while ensuring statistical guarantees. In the forecasting stage, we combine multiple predictions derived from diverse data sources and methodologies for an improved prediction, aiming to optimize the UC performance. In the optimization stage, the combined prediction is used to construct an uncertainty set with statistical guarantees, based on which the robust UC model is formulated. The optimal robust UC solution provides feedback to refine the forecasting process, forming a closed loop. To solve the proposed integrated forecasting-optimization framework efficiently and effectively, we develop a neural network-based surrogate model for acceleration and introduce a reshaping method for the uncertainty set based on the optimization result to reduce conservativeness. Case studies on modified IEEE 30-bus and 118-bus systems demonstrate the advantages of the proposed approach.

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.

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.