IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i16p6091-d1221725.html
   My bibliography  Save this article

Minimization of Economic Losses in Photovoltaic System Cleaning Schedules Based on a Novel Methodological Framework for Performance Ratio Forecast and Cost Analysis

Author

Listed:
  • Fabian Zuñiga-Cortes

    (Electrical and Electronic Engineering Department, Universidad del Valle, Calle 13 # 100-00, Cali 760032, Colombia)

  • Juan D. Garcia-Racines

    (Electrical and Electronic Engineering Department, Universidad del Valle, Calle 13 # 100-00, Cali 760032, Colombia)

  • Eduardo Caicedo-Bravo

    (Electrical and Electronic Engineering Department, Universidad del Valle, Calle 13 # 100-00, Cali 760032, Colombia)

  • Hernan Moncada-Vega

    (Maintenance Planning Department of T&D, Celsia S.A., Calle 15 # 29B-30, Autopista Cali-Yumbo, Yumbo 760502, Colombia)

Abstract

The growing interest in deploying photovoltaic systems and achieving their benefits as sustainable energy supplier raises the need to seek reliable medium-term and long-term operations with optimal performance and efficient use of economic resources. Cleaning scheduling is one of the activities that can positively impact performance. This work proposes a methodological framework to define the optimal scheduling of the cleaning activities of photovoltaic systems. The framework integrates a forecast model of the performance ratio, including the environmental variables’ effect. In addition, an economic analysis involving the economic losses and maintenance costs of cleaning is used. This framework is applied to a case study of a photovoltaic system located in Yumbo, Colombia. Based on the historical data on irradiance, active energy, temperature, rainfall, and wind speed, the obtained forecast model of the photovoltaic system’s performance ratio in a 60-day horizon has a mean absolute percentage error lesser of than 11%. The next cleaning date is forecasted to be beyond the horizon in a 19-day range, which will decrease as time goes by. This framework was applied to historical data and compared to actual cleaning dates performed by the utility company. The results show a loss of USD 33.616 due to unnecessary, early, or late cleaning activities.

Suggested Citation

  • Fabian Zuñiga-Cortes & Juan D. Garcia-Racines & Eduardo Caicedo-Bravo & Hernan Moncada-Vega, 2023. "Minimization of Economic Losses in Photovoltaic System Cleaning Schedules Based on a Novel Methodological Framework for Performance Ratio Forecast and Cost Analysis," Energies, MDPI, vol. 16(16), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:6091-:d:1221725
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/16/6091/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/16/6091/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    2. Ramez Abdallah & Adel Juaidi & Salameh Abdel-Fattah & Mahmoud Qadi & Montaser Shadid & Aiman Albatayneh & Hüseyin Çamur & Amos García-Cruz & Francisco Manzano-Agugliaro, 2022. "The Effects of Soiling and Frequency of Optimal Cleaning of PV Panels in Palestine," Energies, MDPI, vol. 15(12), pages 1-18, June.
    3. Marta Redondo & Carlos A. Platero & Antonio Moset & Fernando Rodríguez & Vicente Donate, 2023. "Soiling Modelling in Large Grid-Connected PV Plants for Cleaning Optimization," Energies, MDPI, vol. 16(2), pages 1-13, January.
    4. Ullah, Zia & Elkadeem, M.R. & Kotb, Kotb M. & Taha, Ibrahim B.M. & Wang, Shaorong, 2021. "Multi-criteria decision-making model for optimal planning of on/off grid hybrid solar, wind, hydro, biomass clean electricity supply," Renewable Energy, Elsevier, vol. 179(C), pages 885-910.
    5. Tossa, Alain K. & Soro, Y.M. & Thiaw, L. & Azoumah, Y. & Sicot, Lionel & Yamegueu, D. & Lishou, Claude & Coulibaly, Y. & Razongles, Guillaume, 2016. "Energy performance of different silicon photovoltaic technologies under hot and harsh climate," Energy, Elsevier, vol. 103(C), pages 261-270.
    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. Marta Redondo & Carlos Antonio Platero & Antonio Moset & Fernando Rodríguez & Vicente Donate, 2024. "Review and Comparison of Methods for Soiling Modeling in Large Grid-Connected PV Plants," Sustainability, MDPI, vol. 16(24), pages 1-18, December.
    2. Parsay, Alireza & Gandomzadeh, Mahdi & Yaghoubi, Ali Akbar & Hoorsun, Arman & Gholami, Aslan & Zandi, Majid & Gavagsaz-ghoachani, Roghayeh & Kazem, Hussein A., 2025. "Enhancing photovoltaic efficiency: An in-depth systematic review and critical analysis of dust monitoring, mitigation, and cleaning techniques," Applied Energy, Elsevier, vol. 388(C).
    3. Janusz Teneta & Mirosław Janowski & Karolina Bender, 2023. "Analysis of the Deposition of Pollutants on the Surface of Photovoltaic Modules," Energies, MDPI, vol. 16(23), pages 1-14, November.
    4. Abdulla, Hind & Sleptchenko, Andrei & Nayfeh, Ammar, 2024. "Photovoltaic systems operation and maintenance: A review and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 195(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. Abdulla, Hind & Sleptchenko, Andrei & Nayfeh, Ammar, 2024. "Photovoltaic systems operation and maintenance: A review and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 195(C).
    2. Fadaki, Masih & Asadikia, Atie, 2024. "Augmenting Monte Carlo Tree Search for managing service level agreements," International Journal of Production Economics, Elsevier, vol. 271(C).
    3. Martínez-Martínez, Yenisleidy & Dewulf, Jo & Casas-Ledón, Yannay, 2022. "GIS-based site suitability analysis and ecosystem services approach for supporting renewable energy development in south-central Chile," Renewable Energy, Elsevier, vol. 182(C), pages 363-376.
    4. Miroslav Navratil & Andrea Kolkova, 2019. "Decomposition and Forecasting Time Series in the Business Economy Using Prophet Forecasting Model," Central European Business Review, Prague University of Economics and Business, vol. 2019(4), pages 26-39.
    5. Yu, Dejian & Xiang, Bo, 2024. "An ESTs detection research based on paper entity mapping: Combining scientific text modeling and neural prophet," Journal of Informetrics, Elsevier, vol. 18(4).
    6. Mohamed Abdel-Basset & Abduallah Gamal & Ibrahim M. Hezam & Karam M. Sallam, 2024. "Sustainability assessment of optimal location of electric vehicle charge stations: a conceptual framework for green energy into smart cities," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(5), pages 11475-11513, May.
    7. Andrea Kolková, 2024. "Data Analysis in Demand Forecasting: A Case Study of Poetry Book Sales in the European Area," Central European Business Review, Prague University of Economics and Business, vol. 2024(5), pages 51-69.
    8. Zhewei Huang & Yawen Yi, 2024. "Short-Term Load Forecasting for Regional Smart Energy Systems Based on Two-Stage Feature Extraction and Hybrid Inverted Transformer," Sustainability, MDPI, vol. 16(17), pages 1-25, September.
    9. Chikh, Madjid & Berkane, Smain & Mahrane, Achour & Sellami, Rabah & Yassaa, Noureddine, 2021. "Performance assessment of a 400 kWp multi- technology photovoltaic grid-connected pilot plant in arid region of Algeria," Renewable Energy, Elsevier, vol. 172(C), pages 488-501.
    10. Balaska, Amira & Tahri, Ali & Tahri, Fatima & Stambouli, Amine Boudghene, 2017. "Performance assessment of five different photovoltaic module technologies under outdoor conditions in Algeria," Renewable Energy, Elsevier, vol. 107(C), pages 53-60.
    11. Elkadeem, Mohamed R. & Kotb, Kotb M. & Abido, Mohamed A. & Hasanien, Hany M. & Atiya, Eman G. & Almakhles, Dhafer & Elmorshedy, Mahmoud F., 2024. "Techno-enviro-socio-economic design and finite set model predictive current control of a grid-connected large-scale hybrid solar/wind energy system: A case study of Sokhna Industrial Zone, Egypt," Energy, Elsevier, vol. 289(C).
    12. Md. Iftekharul Alam Efat & Petr Hajek & Mohammad Zoynul Abedin & Rahat Uddin Azad & Md. Al Jaber & Shuvra Aditya & Mohammad Kabir Hassan, 2024. "Deep-learning model using hybrid adaptive trend estimated series for modelling and forecasting sales," Annals of Operations Research, Springer, vol. 339(1), pages 297-328, August.
    13. Jayesh Thaker & Robert Höller, 2022. "A Comparative Study of Time Series Forecasting of Solar Energy Based on Irradiance Classification," Energies, MDPI, vol. 15(8), pages 1-26, April.
    14. Srinka Basu & Sugata Sen, 2023. "COVID 19 Pandemic, Socio-Economic Behaviour and Infection Characteristics: An Inter-Country Predictive Study Using Deep Learning," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 645-676, February.
    15. Apostolos Giannoulidis & Anastasios Gounaris & Athanasios Naskos & Nikodimos Nikolaidis & Daniel Caljouw, 2025. "Engineering and evaluating an unsupervised predictive maintenance solution: a cold-forming press case-study," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 2121-2139, March.
    16. Yinghui Huang & Hui Liu & Lin Zhang & Shen Li & Weijun Wang & Zhihong Ren & Zongkui Zhou & Xueyao Ma, 2021. "The Psychological and Behavioral Patterns of Online Psychological Help-Seekers before and during COVID-19 Pandemic: A Text Mining-Based Longitudinal Ecological Study," IJERPH, MDPI, vol. 18(21), pages 1-19, November.
    17. Oras Baker & Zahra Ziran & Massimo Mecella & Kasthuri Subaramaniam & Sellappan Palaniappan, 2025. "Predictive Modeling for Pandemic Forecasting: A COVID-19 Study in New Zealand and Partner Countries," IJERPH, MDPI, vol. 22(4), pages 1-22, April.
    18. Junyi Lu & Sebastian Meyer, 2020. "Forecasting Flu Activity in the United States: Benchmarking an Endemic-Epidemic Beta Model," IJERPH, MDPI, vol. 17(4), pages 1-13, February.
    19. Santonab Chakraborty & Himalaya Nirjhar Datta & Kanak Kalita & Shankar Chakraborty, 2023. "A narrative review of multi-objective optimization on the basis of ratio analysis (MOORA) method in decision making," OPSEARCH, Springer;Operational Research Society of India, vol. 60(4), pages 1844-1887, December.
    20. Wellens, Arnoud P. & Boute, Robert N. & Udenio, Maximiliano, 2024. "Simplifying tree-based methods for retail sales forecasting with explanatory variables," European Journal of Operational Research, Elsevier, vol. 314(2), pages 523-539.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:gam:jeners:v:16:y:2023:i:16:p:6091-:d:1221725. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.