Recovering Corrupted Data in Wind Farm Measurements: A Matrix Completion Approach

Mattia Silei, Stefania Bellavia, Francesco Superchi and Alessandro Bianchini ()
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Mattia Silei: Dipartimento di Matematica ed Informatica “Ulisse Dini”, Università degli Studi di Firenze, 50134 Firenze, Italy
Stefania Bellavia: INDAM-GNCS Research Group, 00185 Roma, Italy
Francesco Superchi: Dipartimento di Ingegneria Industriale, Università degli Studi di Firenze, 500139 Firenze, Italy
Alessandro Bianchini: Dipartimento di Ingegneria Industriale, Università degli Studi di Firenze, 500139 Firenze, Italy

Energies, 2023, vol. 16, issue 4, 1-32

Abstract: Availability of reliable and extended datasets of recorded power output from renewables is nowadays seen as one of the key drivers to improve the design and control of smart energy systems. In particular, these datasets are needed to train artificial intelligence methods. Very often, however, datasets can be corrupted due to lack of records connected to failures of the acquisition system, maintenance downtime periods, etc. Several recovery (imputation) methods have been used to guess and replace missing data. In this paper, we exploit the matrix completion approach. The available measures of several variables referring to a real onshore wind farm are organized into a matrix in a daily range and the Singular Value Thresholding method is used to carry out the matrix completion process. Numerical results show that matrix completion is a reliable and parameter-free tuning tool to impute missing data in these applications.

Keywords: corrupted data recovery; matrix completion; data driven; wind farm; wind power (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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