Hourly PV production estimation by means of an exportable multiple linear regression model

Mauricio Trigo-González, F.J. Batlles, Joaquín Alonso-Montesinos, Pablo Ferrada, J. del Sagrado, M. Martínez-Durbán, Marcelo Cortés, Carlos Portillo and Aitor Marzo

Renewable Energy, 2019, vol. 135, issue C, 303-312

Abstract: The current state of photovoltaic (PV) electricity integration is demanding several strategies that control the optimal performance of PV plants. Cleaning the PV plant, controlling PV production or the estimation of the electricity generation, are some relevant items related to the PV systems. In general, the soiling, the clouds and another climatological factorsare involved in the final PV production. For knowing the performance of a PV system, it is necessity to model the PV plant behavior according to these relevant variables. In this work, a Multiple Linear Regression (MLR) model has been presented to determine the hourly PV production by using the Performance Ratio (PR) factor, according to different technologies: Cadmium Telluride (CdTe) and multicrystallinesilicon (mc-Si). In this sense, data from several PV plants were studied in different Chile regions: San Pedro de Atacama and Antofagasta. With this study, it has been determined that the model can be extrapolated to different climatological emplacements, where generally, the root mean square error (RMSE) presents values lower than 16% in all cases, having the best result the CdTetechnology.

Keywords: PV estimation; CdTe; Mc-Si; Multiple linear regression; Solar energy; Performance ratio (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:135:y:2019:i:c:p:303-312

DOI: 10.1016/j.renene.2018.12.014

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