Hourly day ahead solar irradiance forecasting model in labview using cloud cover data

Oguzhan Ceylan; Michael Starke; Phil Irminger; Ben Ollis; Dan King; Kevin Tomsovic

Hourly day ahead solar irradiance forecasting model in labview using cloud cover data

Oguzhan Ceylan, Michael Starke, Phil Irminger, Ben Ollis, Dan King, Kevin Tomsovic

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

This paper applies a regression based numerical method for hourly forecasting photovoltaic power output. This methodology uses a historical dataset composed of irradiance, azimuth, zenith angle and time of day information. A developed forecast program from this methodology pulls publicly available cloud cover forecast data for the following day and uses a numerical regression based method for fitting the data. Using publicly available temperature forecast data, forecasted irradiance data, and computed solar position (zenith, azimuth) data, both power output and temperature module output of PV array is computed. Numerical forecast results are compared to actual collected data.

Original language	English
Pages (from-to)	2047-2054
Number of pages	8
Journal	Istanbul University - Journal of Electrical and Electronics Engineering
Volume	16
Issue number	2
State	Published - 2016

Funding

This work was sponsored by the Office of Electricity Delivery & Energy Reliability, U.S. Department of Energy under Contract No. DE-AC05-00OR 22725 with UT-Battelle and conducted at ORNL and UT Knoxville. This work also made use of Engineering Research Center Shared Facilities supported by the Engineering Research Center Program of the National Science Foundation and the Department of Energy under NSF Award Number EEC-1041877 and the CURENT Industry Partnership Program. The first author would like to thank the Scientific and Technological Research Council of Turkey (TUBITAK) for its financial support.

Funders	Funder number
TUBITAK
National Science Foundation	EEC-1041877
U.S. Department of Energy	DE-AC05-00OR 22725
Office of Electricity Delivery and Energy Reliability
Oak Ridge National Laboratory
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

Keywords

Labview
Numerical regression
Solar forecasting

UN SDGs

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

Cite this

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abstract = "This paper applies a regression based numerical method for hourly forecasting photovoltaic power output. This methodology uses a historical dataset composed of irradiance, azimuth, zenith angle and time of day information. A developed forecast program from this methodology pulls publicly available cloud cover forecast data for the following day and uses a numerical regression based method for fitting the data. Using publicly available temperature forecast data, forecasted irradiance data, and computed solar position (zenith, azimuth) data, both power output and temperature module output of PV array is computed. Numerical forecast results are compared to actual collected data.",

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AU - Ceylan, Oguzhan

AU - Starke, Michael

AU - Irminger, Phil

AU - Ollis, Ben

AU - King, Dan

AU - Tomsovic, Kevin

PY - 2016

Y1 - 2016

N2 - This paper applies a regression based numerical method for hourly forecasting photovoltaic power output. This methodology uses a historical dataset composed of irradiance, azimuth, zenith angle and time of day information. A developed forecast program from this methodology pulls publicly available cloud cover forecast data for the following day and uses a numerical regression based method for fitting the data. Using publicly available temperature forecast data, forecasted irradiance data, and computed solar position (zenith, azimuth) data, both power output and temperature module output of PV array is computed. Numerical forecast results are compared to actual collected data.

AB - This paper applies a regression based numerical method for hourly forecasting photovoltaic power output. This methodology uses a historical dataset composed of irradiance, azimuth, zenith angle and time of day information. A developed forecast program from this methodology pulls publicly available cloud cover forecast data for the following day and uses a numerical regression based method for fitting the data. Using publicly available temperature forecast data, forecasted irradiance data, and computed solar position (zenith, azimuth) data, both power output and temperature module output of PV array is computed. Numerical forecast results are compared to actual collected data.

KW - Labview

KW - Numerical regression

KW - Solar forecasting

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JO - Istanbul University - Journal of Electrical and Electronics Engineering

JF - Istanbul University - Journal of Electrical and Electronics Engineering

IS - 2

ER -

Hourly day ahead solar irradiance forecasting model in labview using cloud cover data

Abstract

Funding

Keywords

UN SDGs

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