Trading data for wind power forecasting: A regression market with lasso regularization

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Electric Power Systems Research

سال: 2022

ISSN: ['1873-2046', '0378-7796']

DOI: https://doi.org/10.1016/j.epsr.2022.108442