Automatic Loss Factor Modeling and Attribution on Unlabeled PV Energy Data

Mayers, Bennet; Deceglie, Michael

doi:10.1109/pvsc57443.2024.10748940

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Automatic Loss Factor Modeling and Attribution on Unlabeled PV Energy Data

Mayers, Bennet·Deceglie, Michael

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Abstract

We present a novel approach for modeling the loss factors of photovoltaic power generation systems (PV systems). This method is a white-box machine learning model built on convex optimization that is fast, interpretable, and auditable. It takes as an input the measured daily energy produced by the system, over a multi-year period, and returns a multiplicative decomposition model of the daily energy signal and full attribution of the total energy loss to each feature. The methods section of this paper has two major components: (1) the description of the signal decomposition (SD) model, expressed in the SD framework, and (2) the attribution of total energy losses via Shapley values. We validate the method on synthetic and open-source data sets and compare to similar methods from the literature.

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artificial intelligence convex optimization degradation interpretable models machine learning MATHEMATICS AND COMPUTING,SOLAR ENERGY PV systems Shapley values soiling

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BibTeX RIS

Mayers, Bennet, Deceglie, Michael. 2024-11-15. Automatic Loss Factor Modeling and Attribution on Unlabeled PV Energy Data. https://doi.org/10.1109/pvsc57443.2024.10748940

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