IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v206y2017icp1464-1483.html
   My bibliography  Save this article

Short-term peer-to-peer solar forecasting in a network of photovoltaic systems

Author

Listed:
  • Elsinga, Boudewijn
  • van Sark, Wilfried G.J.H.M.

Abstract

Solar forecasting is a necessary component of economical realization of high penetration levels of photovoltaic (PV) systems. This paper presents a short term, intra-hour solar forecasting method. This “peer-to-peer” (P2P) forecasting method is based on the cross-correlation time lag between clear-sky index time series of pairs of PV-systems that are influenced by the (assumed) same cloud sequentially, with the feature that the forecast horizon (FH) can be set at a fixed value. The P2P forecasting algorithm was evaluated for 11 central PV-systems (out of 202) over a half year period from the 1st of March through the 31st of August 2015 using the forecast skill (FS) metric. Positive FS means improvement over reference clear-sky index persistence forecasting. The P2P forecasting method was evaluated over a subset of days with either high, all or low irradiance variability. The average forecast skill (avgFS) concerning forecast horizons between 5 and 8min was 5.99%, −1.61% and −16.0% over these periods respectively, indicating the superior performance of the P2P method over persistence during the highly variable days, which are most interesting from the perspective of electricity grid management.

Suggested Citation

  • Elsinga, Boudewijn & van Sark, Wilfried G.J.H.M., 2017. "Short-term peer-to-peer solar forecasting in a network of photovoltaic systems," Applied Energy, Elsevier, vol. 206(C), pages 1464-1483.
    • Handle: RePEc:eee:appene:v:206:y:2017:i:c:p:1464-1483
    DOI: 10.1016/j.apenergy.2017.09.115
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261917314010
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2017.09.115?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    2. Verbong, Geert P.J. & Beemsterboer, Sjouke & Sengers, Frans, 2013. "Smart grids or smart users? Involving users in developing a low carbon electricity economy," Energy Policy, Elsevier, vol. 52(C), pages 117-125.
    3. Yang, Dazhi & Gu, Chaojun & Dong, Zibo & Jirutitijaroen, Panida & Chen, Nan & Walsh, Wilfred M., 2013. "Solar irradiance forecasting using spatial-temporal covariance structures and time-forward kriging," Renewable Energy, Elsevier, vol. 60(C), pages 235-245.
    4. Yang, Dazhi & Sharma, Vishal & Ye, Zhen & Lim, Lihong Idris & Zhao, Lu & Aryaputera, Aloysius W., 2015. "Forecasting of global horizontal irradiance by exponential smoothing, using decompositions," Energy, Elsevier, vol. 81(C), pages 111-119.
    5. Barbieri, Florian & Rajakaruna, Sumedha & Ghosh, Arindam, 2017. "Very short-term photovoltaic power forecasting with cloud modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 242-263.
    6. Kaur, Amanpreet & Nonnenmacher, Lukas & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2016. "Benefits of solar forecasting for energy imbalance markets," Renewable Energy, Elsevier, vol. 86(C), pages 819-830.
    7. van der Kam, Mart & van Sark, Wilfried, 2015. "Smart charging of electric vehicles with photovoltaic power and vehicle-to-grid technology in a microgrid; a case study," Applied Energy, Elsevier, vol. 152(C), pages 20-30.
    8. Matallanas, E. & Castillo-Cagigal, M. & Gutiérrez, A. & Monasterio-Huelin, F. & Caamaño-Martín, E. & Masa, D. & Jiménez-Leube, J., 2012. "Neural network controller for Active Demand-Side Management with PV energy in the residential sector," Applied Energy, Elsevier, vol. 91(1), pages 90-97.
    9. Voyant, Cyril & Muselli, Marc & Paoli, Christophe & Nivet, Marie-Laure, 2011. "Optimization of an artificial neural network dedicated to the multivariate forecasting of daily global radiation," Energy, Elsevier, vol. 36(1), pages 348-359.
    10. Mwasilu, Francis & Justo, Jackson John & Kim, Eun-Kyung & Do, Ton Duc & Jung, Jin-Woo, 2014. "Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 501-516.
    11. Widén, Joakim & Carpman, Nicole & Castellucci, Valeria & Lingfors, David & Olauson, Jon & Remouit, Flore & Bergkvist, Mikael & Grabbe, Mårten & Waters, Rafael, 2015. "Variability assessment and forecasting of renewables: A review for solar, wind, wave and tidal resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 356-375.
    12. Vaz, A.G.R. & Elsinga, B. & van Sark, W.G.J.H.M. & Brito, M.C., 2016. "An artificial neural network to assess the impact of neighbouring photovoltaic systems in power forecasting in Utrecht, the Netherlands," Renewable Energy, Elsevier, vol. 85(C), pages 631-641.
    13. Zagouras, Athanassios & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2015. "On the role of lagged exogenous variables and spatio–temporal correlations in improving the accuracy of solar forecasting methods," Renewable Energy, Elsevier, vol. 78(C), pages 203-218.
    14. Boland, John, 2015. "Spatial-temporal forecasting of solar radiation," Renewable Energy, Elsevier, vol. 75(C), pages 607-616.
    15. Diagne, Maimouna & David, Mathieu & Lauret, Philippe & Boland, John & Schmutz, Nicolas, 2013. "Review of solar irradiance forecasting methods and a proposition for small-scale insular grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 65-76.
    16. Bird, Lori & Lew, Debra & Milligan, Michael & Carlini, E. Maria & Estanqueiro, Ana & Flynn, Damian & Gomez-Lazaro, Emilio & Holttinen, Hannele & Menemenlis, Nickie & Orths, Antje & Eriksen, Peter Børr, 2016. "Wind and solar energy curtailment: A review of international experience," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 577-586.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Terrén-Serrano, G. & Martínez-Ramón, M., 2023. "Kernel learning for intra-hour solar forecasting with infrared sky images and cloud dynamic feature extraction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
    2. Feng, Cong & Zhang, Jie & Zhang, Wenqi & Hodge, Bri-Mathias, 2022. "Convolutional neural networks for intra-hour solar forecasting based on sky image sequences," Applied Energy, Elsevier, vol. 310(C).
    3. Lima, Marcello Anderson F.B. & Carvalho, Paulo C.M. & Fernández-Ramírez, Luis M. & Braga, Arthur P.S., 2020. "Improving solar forecasting using Deep Learning and Portfolio Theory integration," Energy, Elsevier, vol. 195(C).
    4. Xu, Jian & Wang, Jing & Liao, Siyang & Sun, Yuanzhang & Ke, Deping & Li, Xiong & Liu, Ji & Jiang, Yibo & Wei, Congying & Tang, Bowen, 2018. "Stochastic multi-objective optimization of photovoltaics integrated three-phase distribution network based on dynamic scenarios," Applied Energy, Elsevier, vol. 231(C), pages 985-996.
    5. Yang, Dazhi & Yagli, Gokhan Mert & Srinivasan, Dipti, 2022. "Sub-minute probabilistic solar forecasting for real-time stochastic simulations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    6. Rodríguez, Fermín & Martín, Fernando & Fontán, Luis & Galarza, Ainhoa, 2021. "Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power," Energy, Elsevier, vol. 229(C).
    7. Rodríguez, Fermín & Galarza, Ainhoa & Vasquez, Juan C. & Guerrero, Josep M., 2022. "Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control," Energy, Elsevier, vol. 239(PB).
    8. Yang, Dazhi & Gueymard, Christian A., 2019. "Producing high-quality solar resource maps by integrating high- and low-accuracy measurements using Gaussian processes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    9. Llinet Benavides Cesar & Rodrigo Amaro e Silva & Miguel Ángel Manso Callejo & Calimanut-Ionut Cira, 2022. "Review on Spatio-Temporal Solar Forecasting Methods Driven by In Situ Measurements or Their Combination with Satellite and Numerical Weather Prediction (NWP) Estimates," Energies, MDPI, vol. 15(12), pages 1-23, June.
    10. Simone Sala & Alfonso Amendola & Sonia Leva & Marco Mussetta & Alessandro Niccolai & Emanuele Ogliari, 2019. "Comparison of Data-Driven Techniques for Nowcasting Applied to an Industrial-Scale Photovoltaic Plant," Energies, MDPI, vol. 12(23), pages 1-19, November.
    11. Sk Abdul Aleem & S. M. Suhail Hussain & Taha Selim Ustun, 2020. "A Review of Strategies to Increase PV Penetration Level in Smart Grids," Energies, MDPI, vol. 13(3), pages 1-28, February.
    12. Jamal, Taskin & Carter, Craig & Schmidt, Thomas & Shafiullah, G.M. & Calais, Martina & Urmee, Tania, 2019. "An energy flow simulation tool for incorporating short-term PV forecasting in a diesel-PV-battery off-grid power supply system," Applied Energy, Elsevier, vol. 254(C).
    13. Rodríguez-Benítez, Francisco J. & López-Cuesta, Miguel & Arbizu-Barrena, Clara & Fernández-León, María M. & Pamos-Ureña, Miguel Á. & Tovar-Pescador, Joaquín & Santos-Alamillos, Francisco J. & Pozo-Váz, 2021. "Assessment of new solar radiation nowcasting methods based on sky-camera and satellite imagery," Applied Energy, Elsevier, vol. 292(C).
    14. Zheng, Lingwei & Liu, Zhaokun & Shen, Junnan & Wu, Chenxi, 2018. "Very short-term maximum Lyapunov exponent forecasting tool for distributed photovoltaic output," Applied Energy, Elsevier, vol. 229(C), pages 1128-1139.
    15. Dongkyu Lee & Jae-Weon Jeong & Guebin Choi, 2021. "Short Term Prediction of PV Power Output Generation Using Hierarchical Probabilistic Model," Energies, MDPI, vol. 14(10), pages 1-15, May.
    16. Li, Qian & Wu, Zhou & Xia, Xiaohua, 2018. "Estimate and characterize PV power at demand-side hybrid system," Applied Energy, Elsevier, vol. 218(C), pages 66-77.
    17. Litjens, G.B.M.A. & Worrell, E. & van Sark, W.G.J.H.M., 2018. "Assessment of forecasting methods on performance of photovoltaic-battery systems," Applied Energy, Elsevier, vol. 221(C), pages 358-373.
    18. AlSkaif, Tarek & Dev, Soumyabrata & Visser, Lennard & Hossari, Murhaf & van Sark, Wilfried, 2020. "A systematic analysis of meteorological variables for PV output power estimation," Renewable Energy, Elsevier, vol. 153(C), pages 12-22.
    19. Anagnostos, D. & Schmidt, T. & Cavadias, S. & Soudris, D. & Poortmans, J. & Catthoor, F., 2019. "A method for detailed, short-term energy yield forecasting of photovoltaic installations," Renewable Energy, Elsevier, vol. 130(C), pages 122-129.
    20. Erdener, Burcin Cakir & Feng, Cong & Doubleday, Kate & Florita, Anthony & Hodge, Bri-Mathias, 2022. "A review of behind-the-meter solar forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    21. Chen, Xiaoyang & Du, Yang & Lim, Enggee & Fang, Lurui & Yan, Ke, 2022. "Towards the applicability of solar nowcasting: A practice on predictive PV power ramp-rate control," Renewable Energy, Elsevier, vol. 195(C), pages 147-166.
    22. Guilherme Fonseca Bassous & Rodrigo Flora Calili & Carlos Hall Barbosa, 2021. "Development of a Low-Cost Data Acquisition System for Very Short-Term Photovoltaic Power Forecasting," Energies, MDPI, vol. 14(19), pages 1-28, September.
    23. Meng, B. & Loonen, R.C.G.M. & Hensen, J.L.M., 2022. "Performance variability and implications for yield prediction of rooftop PV systems – Analysis of 246 identical systems," Applied Energy, Elsevier, vol. 322(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    2. Llinet Benavides Cesar & Rodrigo Amaro e Silva & Miguel Ángel Manso Callejo & Calimanut-Ionut Cira, 2022. "Review on Spatio-Temporal Solar Forecasting Methods Driven by In Situ Measurements or Their Combination with Satellite and Numerical Weather Prediction (NWP) Estimates," Energies, MDPI, vol. 15(12), pages 1-23, June.
    3. Michel Fliess & Cédric Join & Cyril Voyant, 2018. "Prediction bands for solar energy: New short-term time series forecasting techniques," Post-Print hal-01736518, HAL.
    4. Lan, Hai & Yin, He & Hong, Ying-Yi & Wen, Shuli & Yu, David C. & Cheng, Peng, 2018. "Day-ahead spatio-temporal forecasting of solar irradiation along a navigation route," Applied Energy, Elsevier, vol. 211(C), pages 15-27.
    5. Voyant, Cyril & Motte, Fabrice & Notton, Gilles & Fouilloy, Alexis & Nivet, Marie-Laure & Duchaud, Jean-Laurent, 2018. "Prediction intervals for global solar irradiation forecasting using regression trees methods," Renewable Energy, Elsevier, vol. 126(C), pages 332-340.
    6. Pedregal, Diego J. & Trapero, Juan R., 2021. "Adjusted combination of moving averages: A forecasting system for medium-term solar irradiance," Applied Energy, Elsevier, vol. 298(C).
    7. Anh Ngoc-Lan Huynh & Ravinesh C. Deo & Duc-Anh An-Vo & Mumtaz Ali & Nawin Raj & Shahab Abdulla, 2020. "Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network," Energies, MDPI, vol. 13(14), pages 1-30, July.
    8. Zang, Haixiang & Liu, Ling & Sun, Li & Cheng, Lilin & Wei, Zhinong & Sun, Guoqiang, 2020. "Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations," Renewable Energy, Elsevier, vol. 160(C), pages 26-41.
    9. Shepero, Mahmoud & Munkhammar, Joakim & Widén, Joakim & Bishop, Justin D.K. & Boström, Tobias, 2018. "Modeling of photovoltaic power generation and electric vehicles charging on city-scale: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 61-71.
    10. Hanany Tolba & Nouha Dkhili & Julien Nou & Julien Eynard & Stéphane Thil & Stéphane Grieu, 2020. "Multi-Horizon Forecasting of Global Horizontal Irradiance Using Online Gaussian Process Regression: A Kernel Study," Energies, MDPI, vol. 13(16), pages 1-23, August.
    11. Khan, Zulfiqar Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2023. "Dual stream network with attention mechanism for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 338(C).
    12. Benali, L. & Notton, G. & Fouilloy, A. & Voyant, C. & Dizene, R., 2019. "Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components," Renewable Energy, Elsevier, vol. 132(C), pages 871-884.
    13. Samu, Remember & Calais, Martina & Shafiullah, G.M. & Moghbel, Moayed & Shoeb, Md Asaduzzaman & Nouri, Bijan & Blum, Niklas, 2021. "Applications for solar irradiance nowcasting in the control of microgrids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    14. van der Meer, D.W. & Widén, J. & Munkhammar, J., 2018. "Review on probabilistic forecasting of photovoltaic power production and electricity consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1484-1512.
    15. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    16. Munir Husein & Il-Yop Chung, 2019. "Day-Ahead Solar Irradiance Forecasting for Microgrids Using a Long Short-Term Memory Recurrent Neural Network: A Deep Learning Approach," Energies, MDPI, vol. 12(10), pages 1-21, May.
    17. Sylvain Cros & Jordi Badosa & André Szantaï & Martial Haeffelin, 2020. "Reliability Predictors for Solar Irradiance Satellite-Based Forecast," Energies, MDPI, vol. 13(21), pages 1-21, October.
    18. Demirhan, Haydar & Renwick, Zoe, 2018. "Missing value imputation for short to mid-term horizontal solar irradiance data," Applied Energy, Elsevier, vol. 225(C), pages 998-1012.
    19. du Plessis, A.A. & Strauss, J.M. & Rix, A.J., 2021. "Short-term solar power forecasting: Investigating the ability of deep learning models to capture low-level utility-scale Photovoltaic system behaviour," Applied Energy, Elsevier, vol. 285(C).
    20. Li, Pengtao & Zhou, Kaile & Lu, Xinhui & Yang, Shanlin, 2020. "A hybrid deep learning model for short-term PV power forecasting," Applied Energy, Elsevier, vol. 259(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:206:y:2017:i:c:p:1464-1483. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.