publication . Preprint . 2020

A Temporally Consistent Image-based Sun Tracking Algorithm for Solar Energy Forecasting Applications

Paletta, Quentin; Lasenby, Joan;
Open Access English
  • Published: 02 Dec 2020
Abstract
Comment: Accepted as a workshop paper at NeurIPS 2020
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Related Organizations
Download from
29 references, page 1 of 2

[1] International Energy Agency (IEA). Market Report Series Renewables 2018 Analysis and Forecast to 2023. International Energy Agency, page 211, 2018.

[2] E Ela, V Diakov, E Ibanez, and M Heaney. Impacts of Variability and Uncertainty in Solar Photovoltaic Generation at Multiple Timescales. National Renewable Energy Laboratory, 2013.

[3] Rich H. Inman, Hugo T.C. Pedro, and Carlos F.M. Coimbra. Solar forecasting methods for renewable energy integration. Progress in Energy and Combustion Science, 39(6):535-576, 2013.

[4] Samuel R. West, Daniel Rowe, Saad Sayeef, and Adam Berry. Short-term irradiance forecasting using skycams: Motivation and development. Solar Energy, 110:188-207, 2014.

[5] J. Antonanzas, N. Osorio, R. Escobar, R. Urraca, F. J. Martinez-de Pison, and F. AntonanzasTorres. Review of photovoltaic power forecasting. Solar Energy, 136:78-111, 2016. [OpenAIRE]

[6] Pascal Kuhn, Bijan Nouri, Stefan Wilbert, Christoph Prahl, Nora Kozonek, Thomas Schmidt, Zeyad Yasser, Lourdes Ramirez, Luis Zarzalejo, Angela Meyer, Laurent Vuilleumier, Detlev Heinemann, Philippe Blanc, and Robert Pitz-Paal. Validation of an all-sky imager-based nowcasting system for industrial PV plants. Progress in Photovoltaics: Research and Applications, 26(8):608-621, 2018.

[7] Yinghao Chu, Hugo T.C. Pedro, and Carlos F.M. Coimbra. Hybrid intra-hour DNI forecasts with sky image processing enhanced by stochastic learning. Solar Energy, 98(PC):592-603, 2013.

[8] David Bernecker, Christian Riess, Elli Angelopoulou, and Joachim Hornegger. Continuous short-term irradiance forecasts using sky images. Solar Energy, 110:303-315, 2014.

[9] Viv Bone, John Pidgeon, Michael Kearney, and Ananthanarayanan Veeraragavan. Intra-hour direct normal irradiance forecasting through adaptive clear-sky modelling and cloud tracking. Solar Energy, 159(July 2017):852-867, 2018.

[10] Ibrahim Reda and Afshin Andreas. Solar position algorithm for solar radiation applications. Solar Energy, 76(5):577-589, 2004.

[11] Philippe Blanc and Lucien Wald. The SG2 algorithm for a fast and accurate computation of the position of the Sun for multi-decadal time period. Solar Energy, 86(10):3072-3083, 2012. [OpenAIRE]

[12] M. Haeffelin. SIRTA, a ground-based atmospheric observatory for cloud and aerosol research. Geophysicae, 23:253-275, 2005.

[13] Hugo T.C. Pedro, David P. Larson, and Carlos F.M. Coimbra. A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods. Journal of Renewable and Sustainable Energy, 11(3), 2019.

[14] Yinghao Chu, Mengying Li, and Carlos F.M. Coimbra. Sun-tracking imaging system for intra-hour DNI forecasts. Renewable Energy, 96:792-799, 2016.

[15] Ching Chuan Wei, Yu Chang Song, Chia Chi Chang, and Chuan Bi Lin. Design of a solar tracking system using the brightest region in the sky image sensor. Sensors (Switzerland), 16(12):1-11, 2016.

29 references, page 1 of 2
Abstract
Comment: Accepted as a workshop paper at NeurIPS 2020
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Related Organizations
Download from
29 references, page 1 of 2

[1] International Energy Agency (IEA). Market Report Series Renewables 2018 Analysis and Forecast to 2023. International Energy Agency, page 211, 2018.

[2] E Ela, V Diakov, E Ibanez, and M Heaney. Impacts of Variability and Uncertainty in Solar Photovoltaic Generation at Multiple Timescales. National Renewable Energy Laboratory, 2013.

[3] Rich H. Inman, Hugo T.C. Pedro, and Carlos F.M. Coimbra. Solar forecasting methods for renewable energy integration. Progress in Energy and Combustion Science, 39(6):535-576, 2013.

[4] Samuel R. West, Daniel Rowe, Saad Sayeef, and Adam Berry. Short-term irradiance forecasting using skycams: Motivation and development. Solar Energy, 110:188-207, 2014.

[5] J. Antonanzas, N. Osorio, R. Escobar, R. Urraca, F. J. Martinez-de Pison, and F. AntonanzasTorres. Review of photovoltaic power forecasting. Solar Energy, 136:78-111, 2016. [OpenAIRE]

[6] Pascal Kuhn, Bijan Nouri, Stefan Wilbert, Christoph Prahl, Nora Kozonek, Thomas Schmidt, Zeyad Yasser, Lourdes Ramirez, Luis Zarzalejo, Angela Meyer, Laurent Vuilleumier, Detlev Heinemann, Philippe Blanc, and Robert Pitz-Paal. Validation of an all-sky imager-based nowcasting system for industrial PV plants. Progress in Photovoltaics: Research and Applications, 26(8):608-621, 2018.

[7] Yinghao Chu, Hugo T.C. Pedro, and Carlos F.M. Coimbra. Hybrid intra-hour DNI forecasts with sky image processing enhanced by stochastic learning. Solar Energy, 98(PC):592-603, 2013.

[8] David Bernecker, Christian Riess, Elli Angelopoulou, and Joachim Hornegger. Continuous short-term irradiance forecasts using sky images. Solar Energy, 110:303-315, 2014.

[9] Viv Bone, John Pidgeon, Michael Kearney, and Ananthanarayanan Veeraragavan. Intra-hour direct normal irradiance forecasting through adaptive clear-sky modelling and cloud tracking. Solar Energy, 159(July 2017):852-867, 2018.

[10] Ibrahim Reda and Afshin Andreas. Solar position algorithm for solar radiation applications. Solar Energy, 76(5):577-589, 2004.

[11] Philippe Blanc and Lucien Wald. The SG2 algorithm for a fast and accurate computation of the position of the Sun for multi-decadal time period. Solar Energy, 86(10):3072-3083, 2012. [OpenAIRE]

[12] M. Haeffelin. SIRTA, a ground-based atmospheric observatory for cloud and aerosol research. Geophysicae, 23:253-275, 2005.

[13] Hugo T.C. Pedro, David P. Larson, and Carlos F.M. Coimbra. A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods. Journal of Renewable and Sustainable Energy, 11(3), 2019.

[14] Yinghao Chu, Mengying Li, and Carlos F.M. Coimbra. Sun-tracking imaging system for intra-hour DNI forecasts. Renewable Energy, 96:792-799, 2016.

[15] Ching Chuan Wei, Yu Chang Song, Chia Chi Chang, and Chuan Bi Lin. Design of a solar tracking system using the brightest region in the sky image sensor. Sensors (Switzerland), 16(12):1-11, 2016.

29 references, page 1 of 2
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