Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing

Theocharides, Spyros; Makrides, George; Livera, Andreas; Theristis, Marios; Kaimakis, Paris; Georghiou, George E.

doi:10.1016/j.apenergy.2020.115023

Title: Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing

Abstract

A main challenge towards ensuring large-scale and seamless integration of photovoltaic systems is to improve the accuracy of energy yield forecasts, especially in grid areas of high photovoltaic shares. The scope of this paper is to address this issue by presenting a unified methodology for hourly-averaged day-ahead photovoltaic power forecasts with improved accuracy, based on data-driven machine learning techniques and statistical post-processing. More specifically, the proposed forecasting methodology framework comprised of a data quality stage, data-driven power output machine learning model development (artificial neural networks), weather clustering assessment (K-means clustering), post-processing output optimisation (linear regressive correction method) and the final performance accuracy evaluation. The results showed that the application of linear regression coefficients to the forecasted outputs of the developed day-ahead photovoltaic power production neural network improved the performance accuracy by further correcting solar irradiance forecasting biases. The resulting optimised model provided a mean absolute percentage error of 4.7% when applied to historical system datasets. Finally, the model was validated both, at a hot as well as a cold semi-arid climatic location, and the obtained results demonstrated close agreement by yielding forecasting accuracies of mean absolute percentage error of 4.7% and 6.3%, respectively. Finally, the validation analysis provides evidence thatmore »« less

Authors:

Theocharides, Spyros ^[1]; Makrides, George ^[1]; Livera, Andreas ^[1]; Theristis, Marios ^[2]; Kaimakis, Paris ^[3]; Georghiou, George E. ^[1]

Univ. of Cyprus, Nicosia (Cyprus)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Univ. of Central Lancashire Cyprus, Pyla (Cyprus)

Publication Date:: Tue Apr 21 00:00:00 EDT 2020

Research Org.:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA); European Union (EU)

OSTI Identifier:: 1617300

Report Number(s):: SAND-2020-4201J
Journal ID: ISSN 0306-2619; 685417

Grant/Contract Number:: AC04-94AL85000; SOLAR-ERA.NETT/1215/02; NA0003525

Resource Type:: Accepted Manuscript

Journal Name:: Applied Energy

Additional Journal Information:: Journal Volume: 268; Journal Issue: C; Journal ID: ISSN 0306-2619

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 42 ENGINEERING; artificial neural networks; clustering; forecasting; machine learning; photovoltaic; performance

Citation Formats


                    Theocharides, Spyros, Makrides, George, Livera, Andreas, Theristis, Marios, Kaimakis, Paris, and Georghiou, George E. Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing.  United States: N. p., 2020. 
Web.  doi:10.1016/j.apenergy.2020.115023.

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                    Theocharides, Spyros, Makrides, George, Livera, Andreas, Theristis, Marios, Kaimakis, Paris, & Georghiou, George E. Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing.  United States.  https://doi.org/10.1016/j.apenergy.2020.115023

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                    Theocharides, Spyros, Makrides, George, Livera, Andreas, Theristis, Marios, Kaimakis, Paris, and Georghiou, George E. Tue .  
"Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing".  United States.  https://doi.org/10.1016/j.apenergy.2020.115023.  https://www.osti.gov/servlets/purl/1617300.

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@article{osti_1617300,

  title        = {Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing},

  author       = {Theocharides, Spyros and Makrides, George and Livera, Andreas and Theristis, Marios and Kaimakis, Paris and Georghiou, George E.},

  abstractNote = {A main challenge towards ensuring large-scale and seamless integration of photovoltaic systems is to improve the accuracy of energy yield forecasts, especially in grid areas of high photovoltaic shares. The scope of this paper is to address this issue by presenting a unified methodology for hourly-averaged day-ahead photovoltaic power forecasts with improved accuracy, based on data-driven machine learning techniques and statistical post-processing. More specifically, the proposed forecasting methodology framework comprised of a data quality stage, data-driven power output machine learning model development (artificial neural networks), weather clustering assessment (K-means clustering), post-processing output optimisation (linear regressive correction method) and the final performance accuracy evaluation. The results showed that the application of linear regression coefficients to the forecasted outputs of the developed day-ahead photovoltaic power production neural network improved the performance accuracy by further correcting solar irradiance forecasting biases. The resulting optimised model provided a mean absolute percentage error of 4.7% when applied to historical system datasets. Finally, the model was validated both, at a hot as well as a cold semi-arid climatic location, and the obtained results demonstrated close agreement by yielding forecasting accuracies of mean absolute percentage error of 4.7% and 6.3%, respectively. Finally, the validation analysis provides evidence that the proposed model exhibits high performance in both forecasting accuracy and stability.},

  doi          = {10.1016/j.apenergy.2020.115023},

  journal      = {Applied Energy},

  number       = C,

  volume       = 268,

  place        = {United States},

  year         = {Tue Apr 21 00:00:00 EDT 2020},

  month        = {Tue Apr 21 00:00:00 EDT 2020}

}

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Works referenced in this record:

Voltage stability and sensitivity analysis of grid-connected photovoltaic systems
conference, July 2011

Xue, Yaosuo; Manjrekar, Madhav; Lin, Chenxi
2011 IEEE Power & Energy Society General Meeting, 2011 IEEE Power and Energy Society General Meeting
DOI: 10.1109/PES.2011.6039649

Solar forecasting methods for renewable energy integration
journal, December 2013

Inman, Rich H.; Pedro, Hugo T. C.; Coimbra, Carlos F. M.
Progress in Energy and Combustion Science, Vol. 39, Issue 6
DOI: 10.1016/j.pecs.2013.06.002

Assessment of forecasting techniques for solar power production with no exogenous inputs
journal, July 2012

Pedro, Hugo T. C.; Coimbra, Carlos F. M.
Solar Energy, Vol. 86, Issue 7
DOI: 10.1016/j.solener.2012.04.004

Solar Power Prediction Based on Satellite Images and Support Vector Machine
journal, July 2016

Jang, Han Seung; Bae, Kuk Yeol; Park, Hong-Shik
IEEE Transactions on Sustainable Energy, Vol. 7, Issue 3
DOI: 10.1109/TSTE.2016.2535466

Short-term solar forecasting based on sky images to enable higher PV generation in remote electricity networks
journal, January 2017

Schmidt, Thomas; Calais, Martina; Roy, Eric
Renewable Energy and Environmental Sustainability, Vol. 2
DOI: 10.1051/rees/2017028

Review of photovoltaic power forecasting
journal, October 2016

Antonanzas, J.; Osorio, N.; Escobar, R.
Solar Energy, Vol. 136
DOI: 10.1016/j.solener.2016.06.069

Online 24-h solar power forecasting based on weather type classification using artificial neural network
journal, November 2011

Chen, Changsong; Duan, Shanxu; Cai, Tao
Solar Energy, Vol. 85, Issue 11
DOI: 10.1016/j.solener.2011.08.027

Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines
journal, May 2012

Shi, Jie; Lee, Wei-Jen; Liu, Yongqian
IEEE Transactions on Industry Applications, Vol. 48, Issue 3
DOI: 10.1109/TIA.2012.2190816

A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output
journal, July 2014

Yang, Hong-Tzer; Huang, Chao-Ming; Huang, Yann-Chang
IEEE Transactions on Sustainable Energy, Vol. 5, Issue 3
DOI: 10.1109/TSTE.2014.2313600

Solar and photovoltaic forecasting through post-processing of the Global Environmental Multiscale numerical weather prediction model: Solar and photovoltaic forecasting
journal, November 2011

Pelland, Sophie; Galanis, George; Kallos, George
Progress in Photovoltaics: Research and Applications, Vol. 21, Issue 3
DOI: 10.1002/pip.1180

PVWatts Version 5 Manual
report, September 2014

Dobos, A.
DOI: 10.2172/1158421

Prediction of short-term PV power output and uncertainty analysis
journal, October 2018

Liu, Luyao; Zhao, Yi; Chang, Dongliang
Applied Energy, Vol. 228
DOI: 10.1016/j.apenergy.2018.06.112

Photovoltaic system power forecasting based on combined grey model and BP neural network
conference, September 2011

Wang, Shouxiang; Zhang, Na; Zhao, Yishu
2011 International Conference on Electrical and Control Engineering (ICECE)
DOI: 10.1109/ICECENG.2011.6057634

Short-term forecasting of power production in a large-scale photovoltaic plant
journal, July 2014

Mellit, A.; Massi Pavan, A.; Lughi, V.
Solar Energy, Vol. 105
DOI: 10.1016/j.solener.2014.03.018

A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output
journal, February 2015

Dolara, Alberto; Grimaccia, Francesco; Leva, Sonia
Energies, Vol. 8, Issue 2
DOI: 10.3390/en8021138

Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques
journal, April 2019

Nespoli, Alfredo; Ogliari, Emanuele; Leva, Sonia
Energies, Vol. 12, Issue 9
DOI: 10.3390/en12091621

Accurate photovoltaic power forecasting models using deep LSTM-RNN
journal, October 2017

Abdel-Nasser, Mohamed; Mahmoud, Karar
Neural Computing and Applications, Vol. 31, Issue 7
DOI: 10.1007/s00521-017-3225-z

Pearson's correlation coefficient
journal, July 2012

Sedgwick, P.
BMJ, Vol. 345, Issue jul04 1
DOI: 10.1136/bmj.e4483

Machine learning algorithms for photovoltaic system power output prediction
conference, June 2018

Theocharides, Spyros; Makrides, George; Georghiou, George E.
2018 IEEE International Energy Conference (ENERGYCON)
DOI: 10.1109/ENERGYCON.2018.8398737

Learning representations by back-propagating errors
journal, October 1986

Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J.
Nature, Vol. 323, Issue 6088
DOI: 10.1038/323533a0

Bayesian Interpolation
journal, May 1992

MacKay, David J. C.
Neural Computation, Vol. 4, Issue 3
DOI: 10.1162/neco.1992.4.3.415

Gauss-Newton approximation to Bayesian learning
conference, January 1997

Dan Foresee, F.; Hagan, M. T.
Proceedings of International Conference on Neural Networks (ICNN'97)
DOI: 10.1109/ICNN.1997.614194

Solar position algorithm for solar radiation applications
journal, January 2004

Reda, Ibrahim; Andreas, Afshin
Solar Energy, Vol. 76, Issue 5
DOI: 10.1016/j.solener.2003.12.003

The Application of Cluster Analysis in Strategic Management Research: an Analysis and Critique
journal, June 1996

KETCHEN Jr., David J.; Shook, Christopher L.
Strategic Management Journal, Vol. 17, Issue 6
DOI: 10.1002/(SICI)1097-0266(199606)17:6<441::AID-SMJ819>3.0.CO;2-G

A New Sub-topics Clustering Method Based on Semi-supervised Learing
journal, October 2012

Xu, Xiaodan
Journal of Computers, Vol. 7, Issue 10
DOI: 10.4304/jcp.7.10.2471-2478

The Royal London Space Planning: An integration of space analysis and treatment planning
journal, October 2000

Kirschen, Robert H.; O'Higgins, Elizabeth A.; Lee, Robert T.
American Journal of Orthodontics and Dentofacial Orthopedics, Vol. 118, Issue 4
DOI: 10.1067/mod.2000.109031

Making data structures persistent
journal, February 1989

Driscoll, James R.; Sarnak, Neil; Sleator, Daniel D.
Journal of Computer and System Sciences, Vol. 38, Issue 1
DOI: 10.1016/0022-0000(89)90034-2

Another look at measures of forecast accuracy
journal, October 2006

Hyndman, Rob J.; Koehler, Anne B.
International Journal of Forecasting, Vol. 22, Issue 4
DOI: 10.1016/j.ijforecast.2006.03.001

Information-Based Skill Scores for Probabilistic Forecasts
journal, January 2008

Ahrens, Bodo; Walser, André
Monthly Weather Review, Vol. 136, Issue 1
DOI: 10.1175/2007MWR1931.1

Updated world map of the Köppen-Geiger climate classification
journal, January 2007

Peel, M. C.; Finlayson, B. L.; McMahon, T. A.
Hydrology and Earth System Sciences, Vol. 11, Issue 5
DOI: 10.5194/hess-11-1633-2007

Quality of performance assessment of PV plants based on irradiation maps
journal, November 2008

Drews, A.; Beyer, H. G.; Rindelhardt, U.
Solar Energy, Vol. 82, Issue 11
DOI: 10.1016/j.solener.2008.04.009

Energy yield prediction errors and uncertainties of different photovoltaic models: Energy yield prediction errors and uncertainties
journal, November 2011

Makrides, George; Zinsser, Bastian; Schubert, Markus
Progress in Photovoltaics: Research and Applications, Vol. 21, Issue 4
DOI: 10.1002/pip.1218

Potential of photovoltaic systems in countries with high solar irradiation
journal, February 2010

Makrides, George; Zinsser, Bastian; Norton, Matthew
Renewable and Sustainable Energy Reviews, Vol. 14, Issue 2
DOI: 10.1016/j.rser.2009.07.021

Numerical weather prediction
journal, December 2002

Kimura, Ryuji
Journal of Wind Engineering and Industrial Aerodynamics, Vol. 90, Issue 12-15
DOI: 10.1016/S0167-6105(02)00261-1

The origins of computer weather prediction and climate modeling
journal, March 2008

Lynch, Peter
Journal of Computational Physics, Vol. 227, Issue 7
DOI: 10.1016/j.jcp.2007.02.034

A forecast error correction method in numerical weather prediction by using recent multiple-time evolution data
journal, August 2013

Xue, Hai-Le; Shen, Xue-Shun; Chou, Ji-Fan
Advances in Atmospheric Sciences, Vol. 30, Issue 5
DOI: 10.1007/s00376-013-2274-1

The Solar Forecast Arbiter: An Open Source Evaluation Framework for Solar Forecasting
conference, June 2019

Hansen, Clifford W.; Holmgren, William F.; Tuohy, Aidan
2019 IEEE 46th Photovoltaic Specialists Conference (PVSC)
DOI: 10.1109/PVSC40753.2019.8980713

Early Stopping - But When?
book, January 1998

Prechelt, Lutz
Lecture Notes in Computer Science
DOI: 10.1007/3-540-49430-8_3

Summarizing multiple aspects of model performance in a single diagram
journal, April 2001

Taylor, Karl E.
Journal of Geophysical Research: Atmospheres, Vol. 106, Issue D7
DOI: 10.1029/2000JD900719

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Title: Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing

Abstract

Citation Formats

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Solar forecasting methods for renewable energy integration journal, December 2013

Assessment of forecasting techniques for solar power production with no exogenous inputs journal, July 2012

Solar Power Prediction Based on Satellite Images and Support Vector Machine journal, July 2016

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Review of photovoltaic power forecasting journal, October 2016

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Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines journal, May 2012

A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output journal, July 2014

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PVWatts Version 5 Manual report, September 2014

Prediction of short-term PV power output and uncertainty analysis journal, October 2018

Photovoltaic system power forecasting based on combined grey model and BP neural network conference, September 2011

Short-term forecasting of power production in a large-scale photovoltaic plant journal, July 2014

A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output journal, February 2015

Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques journal, April 2019

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Pearson's correlation coefficient journal, July 2012

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The Application of Cluster Analysis in Strategic Management Research: an Analysis and Critique journal, June 1996

A New Sub-topics Clustering Method Based on Semi-supervised Learing journal, October 2012

The Royal London Space Planning: An integration of space analysis and treatment planning journal, October 2000

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Energy yield prediction errors and uncertainties of different photovoltaic models: Energy yield prediction errors and uncertainties journal, November 2011

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The origins of computer weather prediction and climate modeling journal, March 2008

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Voltage stability and sensitivity analysis of grid-connected photovoltaic systems
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Online 24-h solar power forecasting based on weather type classification using artificial neural network
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Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines
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A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output
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Solar and photovoltaic forecasting through post-processing of the Global Environmental Multiscale numerical weather prediction model: Solar and photovoltaic forecasting
journal, November 2011

PVWatts Version 5 Manual
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Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques
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Learning representations by back-propagating errors
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Gauss-Newton approximation to Bayesian learning
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journal, January 2004

The Application of Cluster Analysis in Strategic Management Research: an Analysis and Critique
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Making data structures persistent
journal, February 1989

Another look at measures of forecast accuracy
journal, October 2006

Information-Based Skill Scores for Probabilistic Forecasts
journal, January 2008

Updated world map of the Köppen-Geiger climate classification
journal, January 2007

Quality of performance assessment of PV plants based on irradiation maps
journal, November 2008

Energy yield prediction errors and uncertainties of different photovoltaic models: Energy yield prediction errors and uncertainties
journal, November 2011

Potential of photovoltaic systems in countries with high solar irradiation
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journal, December 2002

The origins of computer weather prediction and climate modeling
journal, March 2008

A forecast error correction method in numerical weather prediction by using recent multiple-time evolution data
journal, August 2013

The Solar Forecast Arbiter: An Open Source Evaluation Framework for Solar Forecasting
conference, June 2019

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