Improving WRF GHI forecasts with model output statistics

Burak Barutcu; Tilev Tanriover Seyda Tilev Tanriover; Serim Sakarya; Selahattin Incecik; F. Mert Sayinta; Erhan Caliskan; Abdullah Kahraman; Bulent Aksoy; Ceyhan Kahya; Sema Topcu

doi:10.1007/978-3-319-16709-1_20

Improving WRF GHI forecasts with model output statistics

Burak Barutcu^*, Tilev Tanriover Seyda Tilev Tanriover, Serim Sakarya, Selahattin Incecik, F. Mert Sayinta, Erhan Caliskan, Abdullah Kahraman, Bulent Aksoy, Ceyhan Kahya, Sema Topcu

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

1 Citation (Scopus)

Abstract

Solar energy applications need reliable forecasting of solar irradiance. In this study, we present an assessment of a short-term global horizontal irradiance forecasting system based on Advanced Research Weather Research and Forecasting (WRF-ARW) meteorological model and neural networks as a post-processing method to improve the skill of the system in a highly favorable location for the utilization of solar power in Turkey. The WRF model was used to produce 1 month of 3 days ahead solar irradiance forecasts covering Southeastern Anatolia of Turkey with a horizontal resolution of 4 km. Single-input single-output (SISO) and multi-input single-output (MISO) artificial neural networks (ANN) were used. Furthermore, the overall results of the forecasting system were evaluated by means of statistical indicators: mean bias error, relative mean bias error, root mean square error, and relative root mean square error. The MISO ANN gives better results than the SISO ANN in terms of improving the model predictions, provided by WRF-ARW simulations for August 2011.

Original language	English
Title of host publication	Analysis and Modeling
Publisher	Springer International Publishing
Pages	291-299
Number of pages	9
Volume	1
ISBN (Electronic)	9783319167091
ISBN (Print)	9783319167084
DOIs	https://doi.org/10.1007/978-3-319-16709-1_20
Publication status	Published - 27 Aug 2015

Bibliographical note

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© Springer International Publishing Switzerland 2015. All rights reserved.

Keywords

Artificial neural networks
Model output statistics
Turkey
Weather research and forecasting

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-319-16709-1_20

Cite this

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abstract = "Solar energy applications need reliable forecasting of solar irradiance. In this study, we present an assessment of a short-term global horizontal irradiance forecasting system based on Advanced Research Weather Research and Forecasting (WRF-ARW) meteorological model and neural networks as a post-processing method to improve the skill of the system in a highly favorable location for the utilization of solar power in Turkey. The WRF model was used to produce 1 month of 3 days ahead solar irradiance forecasts covering Southeastern Anatolia of Turkey with a horizontal resolution of 4 km. Single-input single-output (SISO) and multi-input single-output (MISO) artificial neural networks (ANN) were used. Furthermore, the overall results of the forecasting system were evaluated by means of statistical indicators: mean bias error, relative mean bias error, root mean square error, and relative root mean square error. The MISO ANN gives better results than the SISO ANN in terms of improving the model predictions, provided by WRF-ARW simulations for August 2011.",

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AU - Barutcu, Burak

AU - Seyda Tilev Tanriover, Tilev Tanriover

AU - Sakarya, Serim

AU - Incecik, Selahattin

AU - Mert Sayinta, F.

AU - Caliskan, Erhan

AU - Kahraman, Abdullah

AU - Aksoy, Bulent

AU - Kahya, Ceyhan

AU - Topcu, Sema

PY - 2015/8/27

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N2 - Solar energy applications need reliable forecasting of solar irradiance. In this study, we present an assessment of a short-term global horizontal irradiance forecasting system based on Advanced Research Weather Research and Forecasting (WRF-ARW) meteorological model and neural networks as a post-processing method to improve the skill of the system in a highly favorable location for the utilization of solar power in Turkey. The WRF model was used to produce 1 month of 3 days ahead solar irradiance forecasts covering Southeastern Anatolia of Turkey with a horizontal resolution of 4 km. Single-input single-output (SISO) and multi-input single-output (MISO) artificial neural networks (ANN) were used. Furthermore, the overall results of the forecasting system were evaluated by means of statistical indicators: mean bias error, relative mean bias error, root mean square error, and relative root mean square error. The MISO ANN gives better results than the SISO ANN in terms of improving the model predictions, provided by WRF-ARW simulations for August 2011.

AB - Solar energy applications need reliable forecasting of solar irradiance. In this study, we present an assessment of a short-term global horizontal irradiance forecasting system based on Advanced Research Weather Research and Forecasting (WRF-ARW) meteorological model and neural networks as a post-processing method to improve the skill of the system in a highly favorable location for the utilization of solar power in Turkey. The WRF model was used to produce 1 month of 3 days ahead solar irradiance forecasts covering Southeastern Anatolia of Turkey with a horizontal resolution of 4 km. Single-input single-output (SISO) and multi-input single-output (MISO) artificial neural networks (ANN) were used. Furthermore, the overall results of the forecasting system were evaluated by means of statistical indicators: mean bias error, relative mean bias error, root mean square error, and relative root mean square error. The MISO ANN gives better results than the SISO ANN in terms of improving the model predictions, provided by WRF-ARW simulations for August 2011.

KW - Artificial neural networks

KW - Model output statistics

KW - Turkey

KW - Weather research and forecasting

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ER -

Improving WRF GHI forecasts with model output statistics

Abstract

Bibliographical note

Keywords

UN SDGs

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