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Title: Evaluation of Data-Driven Models for Predicting Solar Photovoltaics Power Output

Abstract

This research was undertaken to evaluate different inverse models for predicting power output of solar photovoltaic (PV) systems under different practical scenarios. In particular, we have investigated whether PV power output prediction accuracy can be improved if module/cell temperature was measured in addition to climatic variables, and also the extent to which prediction accuracy degrades if solar irradiation is not measured on the plane of array but only on a horizontal surface. We have also investigated the significance of different independent or regressor variables, such as wind velocity and incident angle modifier in predicting PV power output and cell temperature. The inverse regression model forms have been evaluated both in terms of their goodness-of-fit, and their accuracy and robustness in terms of their predictive performance. Given the accuracy of the measurements, expected CV-RMSE of hourly power output prediction over the year varies between 3.2% and 8.6% when only climatic data are used. Depending on what type of measured climatic and PV performance data is available, different scenarios have been identified and the corresponding appropriate modeling pathways have been proposed. The corresponding models are to be implemented on a controller platform for optimum operational planning of microgrids and integrated energy systems.

Authors:
 [1];  [1];  [2]
  1. Arizona State Univ., Tempe, AZ (United States). School of Sustainable Engineering and the Built Environment
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
OSTI Identifier:
1395284
Grant/Contract Number:
AC05-76RL01830
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Energy (Oxford)
Additional Journal Information:
Journal Name: Energy (Oxford); Journal Volume: 142; Journal ID: ISSN 0360-5442
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; 29 ENERGY PLANNING, POLICY, AND ECONOMY; solar photovoltaics; PV power prediction; data-driven modeling; renewable energy; sustainable energy

Citation Formats

Moslehi, Salim, Reddy, T. Agami, and Katipamula, Srinivas. Evaluation of Data-Driven Models for Predicting Solar Photovoltaics Power Output. United States: N. p., 2017. Web. doi:10.1016/J.ENERGY.2017.09.042.
Moslehi, Salim, Reddy, T. Agami, & Katipamula, Srinivas. Evaluation of Data-Driven Models for Predicting Solar Photovoltaics Power Output. United States. doi:10.1016/J.ENERGY.2017.09.042.
Moslehi, Salim, Reddy, T. Agami, and Katipamula, Srinivas. Sun . "Evaluation of Data-Driven Models for Predicting Solar Photovoltaics Power Output". United States. doi:10.1016/J.ENERGY.2017.09.042.
@article{osti_1395284,
title = {Evaluation of Data-Driven Models for Predicting Solar Photovoltaics Power Output},
author = {Moslehi, Salim and Reddy, T. Agami and Katipamula, Srinivas},
abstractNote = {This research was undertaken to evaluate different inverse models for predicting power output of solar photovoltaic (PV) systems under different practical scenarios. In particular, we have investigated whether PV power output prediction accuracy can be improved if module/cell temperature was measured in addition to climatic variables, and also the extent to which prediction accuracy degrades if solar irradiation is not measured on the plane of array but only on a horizontal surface. We have also investigated the significance of different independent or regressor variables, such as wind velocity and incident angle modifier in predicting PV power output and cell temperature. The inverse regression model forms have been evaluated both in terms of their goodness-of-fit, and their accuracy and robustness in terms of their predictive performance. Given the accuracy of the measurements, expected CV-RMSE of hourly power output prediction over the year varies between 3.2% and 8.6% when only climatic data are used. Depending on what type of measured climatic and PV performance data is available, different scenarios have been identified and the corresponding appropriate modeling pathways have been proposed. The corresponding models are to be implemented on a controller platform for optimum operational planning of microgrids and integrated energy systems.},
doi = {10.1016/J.ENERGY.2017.09.042},
journal = {Energy (Oxford)},
number = ,
volume = 142,
place = {United States},
year = {Sun Sep 10 00:00:00 EDT 2017},
month = {Sun Sep 10 00:00:00 EDT 2017}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on September 10, 2018
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