IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v66y2014icp78-89.html
   My bibliography  Save this article

An ARMAX model for forecasting the power output of a grid connected photovoltaic system

Author

Listed:
  • Li, Yanting
  • Su, Yan
  • Shu, Lianjie

Abstract

Power forecasting has received a great deal of attention due to its importance for planning the operations of photovoltaic (PV) system. Compared to other forecasting techniques, the ARIMA time series model does not require the meteorological forecast of solar irradiance that is often complicated. Due to its simplicity, the ARIMA model has been widely discussed as a statistical model for forecasting power output from a PV system. However, the ARIMA model is a data-driven model that cannot take the climatic information into account. Intuitively, such information is valuable for improving the forecast accuracy. Motivated by this, this paper suggests a generalized model, the ARMAX model, to allow for exogenous inputs for forecasting power output. The suggested model takes temperature, precipitation amount, insolation duration, and humidity that can be easily accessed from the local observatory as exogenous inputs. As the ARMAX model does not rely forecast on solar irradiance, it maintains simplicity as the conventional ARIMA model. On the other hand, it is more general and flexible for practical use than the ARIMA model. It is shown that the ARMAX model greatly improves the forecast accuracy of power output over the ARIMA model. The results were validated based on a grid-connected 2.1 kW PV system.

Suggested Citation

  • Li, Yanting & Su, Yan & Shu, Lianjie, 2014. "An ARMAX model for forecasting the power output of a grid connected photovoltaic system," Renewable Energy, Elsevier, vol. 66(C), pages 78-89.
    • Handle: RePEc:eee:renene:v:66:y:2014:i:c:p:78-89
    DOI: 10.1016/j.renene.2013.11.067
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148113006551
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2013.11.067?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. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    2. Safi, S. & Zeroual, A. & Hassani, M., 2002. "Prediction of global daily solar radiation using higher order statistics," Renewable Energy, Elsevier, vol. 27(4), pages 647-666.
    3. Strzalka, Aneta & Alam, Nazmul & Duminil, Eric & Coors, Volker & Eicker, Ursula, 2012. "Large scale integration of photovoltaics in cities," Applied Energy, Elsevier, vol. 93(C), pages 413-421.
    4. Alam Hossain Mondal, Md. & Sadrul Islam, A.K.M., 2011. "Potential and viability of grid-connected solar PV system in Bangladesh," Renewable Energy, Elsevier, vol. 36(6), pages 1869-1874.
    5. Wong, S.L. & Wan, Kevin K.W. & Lam, Tony N.T., 2010. "Artificial neural networks for energy analysis of office buildings with daylighting," Applied Energy, Elsevier, vol. 87(2), pages 551-557, February.
    6. Dalton, G.J. & Lockington, D.A. & Baldock, T.E., 2009. "Feasibility analysis of renewable energy supply options for a grid-connected large hotel," Renewable Energy, Elsevier, vol. 34(4), pages 955-964.
    7. Fernandez-Jimenez, L. Alfredo & Muñoz-Jimenez, Andrés & Falces, Alberto & Mendoza-Villena, Montserrat & Garcia-Garrido, Eduardo & Lara-Santillan, Pedro M. & Zorzano-Alba, Enrique & Zorzano-Santamaria,, 2012. "Short-term power forecasting system for photovoltaic plants," Renewable Energy, Elsevier, vol. 44(C), pages 311-317.
    8. Mohandes, M. & Rehman, S. & Halawani, T.O., 1998. "Estimation of global solar radiation using artificial neural networks," Renewable Energy, Elsevier, vol. 14(1), pages 179-184.
    Full references (including those not matched with items on IDEAS)

    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. Wang, Guochang & Su, Yan & Shu, Lianjie, 2016. "One-day-ahead daily power forecasting of photovoltaic systems based on partial functional linear regression models," Renewable Energy, Elsevier, vol. 96(PA), pages 469-478.
    2. Ali, Ihsan & Shafiullah, GM & Urmee, Tania, 2018. "A preliminary feasibility of roof-mounted solar PV systems in the Maldives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 18-32.
    3. Yang, Ying & Campana, Pietro Elia & Stridh, Bengt & Yan, Jinyue, 2020. "Potential analysis of roof-mounted solar photovoltaics in Sweden," Applied Energy, Elsevier, vol. 279(C).
    4. Shubham Gupta & Amit Kumar Singh & Sachin Mishra & Pradeep Vishnuram & Nagaraju Dharavat & Narayanamoorthi Rajamanickam & Ch. Naga Sai Kalyan & Kareem M. AboRas & Naveen Kumar Sharma & Mohit Bajaj, 2023. "Estimation of Solar Radiation with Consideration of Terrestrial Losses at a Selected Location—A Review," Sustainability, MDPI, vol. 15(13), pages 1-29, June.
    5. Antonello Rosato & Rosa Altilio & Rodolfo Araneo & Massimo Panella, 2017. "Prediction in Photovoltaic Power by Neural Networks," Energies, MDPI, vol. 10(7), pages 1-25, July.
    6. Li, Yanting & He, Yong & Su, Yan & Shu, Lianjie, 2016. "Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines," Applied Energy, Elsevier, vol. 180(C), pages 392-401.
    7. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    8. Hamed Khodayar Sahebi & Siamak Hoseinzadeh & Hossein Ghadamian & Mohammad Hadi Ghasemi & Farbod Esmaeilion & Davide Astiaso Garcia, 2021. "Techno-Economic Analysis and New Design of a Photovoltaic Power Plant by a Direct Radiation Amplification System," Sustainability, MDPI, vol. 13(20), pages 1-18, October.
    9. Alexandru Pîrjan & Simona-Vasilica Oprea & George Căruțașu & Dana-Mihaela Petroșanu & Adela Bâra & Cristina Coculescu, 2017. "Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers," Energies, MDPI, vol. 10(11), pages 1-36, October.
    10. Verdone, Alessio & Scardapane, Simone & Panella, Massimo, 2024. "Explainable Spatio-Temporal Graph Neural Networks for multi-site photovoltaic energy production," Applied Energy, Elsevier, vol. 353(PB).
    11. Ramos, Francisco López & Batres, Rafael & De-la-Cruz-Márquez, Cynthia Griselle & Anzures, Melina López, 2023. "Optimization models for nopal crop planning with land usage expansion and government subsidy," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    12. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    13. Velo, R. & Osorio, L. & Fernández, M.D. & Rodríguez, M.R., 2014. "An economic analysis of a stand-alone and grid-connected cattle farm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 883-890.
    14. Baloglu, Ulas Baran & Demir, Yakup, 2018. "Lightweight privacy-preserving data aggregation scheme for smart grid metering infrastructure protection," International Journal of Critical Infrastructure Protection, Elsevier, vol. 22(C), pages 16-24.
    15. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    16. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    17. Chen, Jiandong & Xu, Chong & Shahbaz, Muhammad & Song, Malin, 2021. "Interaction determinants and projections of China’s energy consumption: 1997–2030," Applied Energy, Elsevier, vol. 283(C).
    18. Daniela Pencheva, 2020. "Use of Factors Related to the Consumption of Fast Moving Consumer Goods in Business Intelligence System for Managing Orders to Suppliers in Retail Chain," Izvestia Journal of the Union of Scientists - Varna. Economic Sciences Series, Union of Scientists - Varna, Economic Sciences Section, vol. 9(2), pages 124-135, August.
    19. Aurelia Rybak & Aleksandra Rybak & Spas D. Kolev, 2023. "Modeling the Photovoltaic Power Generation in Poland in the Light of PEP2040: An Application of Multiple Regression," Energies, MDPI, vol. 16(22), pages 1-17, November.
    20. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.

    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:renene:v:66:y:2014:i:c:p:78-89. 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.journals.elsevier.com/renewable-energy .

    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.