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This paper proposes a regression market for wind agents to monetize datatraded among themselves for wind power forecasting. Existing literature on datamarkets often treats data disclosure as a binary choice or modulates the dataquality based on the mismatch between the offer and bid prices. As a result,the market disadvantages either the data sellers due to the overestimation oftheir willingness to disclose data, or the data buyers due to the lack ofuseful data being provided. Our proposed regression market determines the datapayment based on the least absolute shrinkage and selection operator (lasso),which not only provides the data buyer with a means for selecting usefulfeatures, but also enables each data seller to individualize the threshold fordata payment. Using both synthetic data and real-world wind data, the casestudies demonstrate a reduction in the overall losses for wind agents who buydata, as well as additional financial benefits to those who sell data.

Trading Data for Wind Power Forecasting: A Regression Market with Lasso Regularization