IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i9p3581-d1382075.html
   My bibliography  Save this article

Prediction of Losses Due to Dust in PV Using Hybrid LSTM-KNN Algorithm: The Case of Saruhanlı

Author

Listed:
  • Tuba Tanyıldızı Ağır

    (Department of Electronic and Automation, Vocational School, Batman University, Batman 72100, Turkey)

Abstract

Sustainable and renewable energy sources are of great importance in today’s world. In this respect, renewable energy sources are used in many fields of technology. In order to minimize dust on PV panels and ensure their sustainability, power losses due to dust must be estimated accurately. In this way, the efficiency of a sustainable energy source will increase and serious economic savings can be achieved. In this study, a hybrid deep learning model was designed to predict losses caused by dust in PV panels installed in the Manisa Saruhanlı district. The hybrid deep learning model consists of Long Short-Term Memory (LSTM) and K-Nearest-Neighbors (KNN) algorithms. The performance of the proposed hybrid deep learning model was compared with LSTM and KNN algorithms. Sensitivity analysis was performed to statistically evaluate the prediction results. The input variables of the models were time, sunshine duration, humidity, ambient temperature and solar radiation. The output variable was the losses caused by dust in the PV panels. Hybrid LSTM-KNN, LSTM and KNN models predicted losses caused by dust in PV panels with 98.22%, 95.51% and 61.49% accuracy. The hybrid LSTM-KNN model predicted losses caused by dust in PV panels with higher accuracy than other models. Using LSTM and KNN algorithms together improved the performance of the hybrid deep learning model. With sensitivity analysis, it was found that solar radiation is the most important variable affecting the losses caused by dust in PV panels.

Suggested Citation

  • Tuba Tanyıldızı Ağır, 2024. "Prediction of Losses Due to Dust in PV Using Hybrid LSTM-KNN Algorithm: The Case of Saruhanlı," Sustainability, MDPI, vol. 16(9), pages 1-20, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:9:p:3581-:d:1382075
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/9/3581/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/9/3581/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Massi Pavan, A. & Mellit, A. & De Pieri, D. & Kalogirou, S.A., 2013. "A comparison between BNN and regression polynomial methods for the evaluation of the effect of soiling in large scale photovoltaic plants," Applied Energy, Elsevier, vol. 108(C), pages 392-401.
    2. Yamaç, Sevim Seda & Todorovic, Mladen, 2020. "Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data," Agricultural Water Management, Elsevier, vol. 228(C).
    3. Kumar Ganti, Praful & Naik, Hrushikesh & Kanungo Barada, Mohanty, 2022. "Environmental impact analysis and enhancement of factors affecting the photovoltaic (PV) energy utilization in mining industry by sparrow search optimization based gradient boosting decision tree appr," Energy, Elsevier, vol. 244(PA).
    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. Patricio Vladimir Méndez-Zambrano & Luis Patricio Tierra Pérez & Rogelio Estalin Ureta Valdez & Ángel Patricio Flores Orozco, 2023. "Technological Innovations for Agricultural Production from an Environmental Perspective: A Review," Sustainability, MDPI, vol. 15(22), pages 1-15, November.
    2. Sueyoshi, Toshiyuki & Goto, Mika, 2017. "Measurement of returns to scale on large photovoltaic power stations in the United States and Germany," Energy Economics, Elsevier, vol. 64(C), pages 306-320.
    3. Yamaç, Sevim Seda, 2021. "Artificial intelligence methods reliably predict crop evapotranspiration with different combinations of meteorological data for sugar beet in a semiarid area," Agricultural Water Management, Elsevier, vol. 254(C).
    4. Sueyoshi, Toshiyuki & Goto, Mika, 2014. "Photovoltaic power stations in Germany and the United States: A comparative study by data envelopment analysis," Energy Economics, Elsevier, vol. 42(C), pages 271-288.
    5. Harrou, Fouzi & Sun, Ying & Taghezouit, Bilal & Saidi, Ahmed & Hamlati, Mohamed-Elkarim, 2018. "Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches," Renewable Energy, Elsevier, vol. 116(PA), pages 22-37.
    6. Bianchini, Augusto & Gambuti, Michele & Pellegrini, Marco & Saccani, Cesare, 2016. "Performance analysis and economic assessment of different photovoltaic technologies based on experimental measurements," Renewable Energy, Elsevier, vol. 85(C), pages 1-11.
    7. Conceição, Ricardo & González-Aguilar, José & Merrouni, Ahmed Alami & Romero, Manuel, 2022. "Soiling effect in solar energy conversion systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    8. Manish Kumar & Anuradha Kumari & Daniel Prakash Kushwaha & Pravendra Kumar & Anurag Malik & Rawshan Ali & Alban Kuriqi, 2020. "Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India," Sustainability, MDPI, vol. 12(19), pages 1-21, September.
    9. Hamzeh F. Assous & Hazem AL-Najjar & Nadia Al-Rousan & Dania AL-Najjar, 2023. "Developing a Sustainable Machine Learning Model to Predict Crop Yield in the Gulf Countries," Sustainability, MDPI, vol. 15(12), pages 1-21, June.
    10. Cai, Baoping & Liu, Yonghong & Ma, Yunpeng & Huang, Lei & Liu, Zengkai, 2015. "A framework for the reliability evaluation of grid-connected photovoltaic systems in the presence of intermittent faults," Energy, Elsevier, vol. 93(P2), pages 1308-1320.
    11. Granata, Francesco & Di Nunno, Fabio, 2021. "Forecasting evapotranspiration in different climates using ensembles of recurrent neural networks," Agricultural Water Management, Elsevier, vol. 255(C).
    12. Obara, Shin’ya & Konno, Daisuke & Utsugi, Yuta & Morel, Jorge, 2014. "Analysis of output power and capacity reduction in electrical storage facilities by peak shift control of PV system with bifacial modules," Applied Energy, Elsevier, vol. 128(C), pages 35-48.
    13. Phon Sheng Hou & Lokman Mohd Fadzil & Selvakumar Manickam & Mahmood A. Al-Shareeda, 2023. "Vector Autoregression Model-Based Forecasting of Reference Evapotranspiration in Malaysia," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
    14. Mellit, A. & Tina, G.M. & Kalogirou, S.A., 2018. "Fault detection and diagnosis methods for photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1-17.
    15. Hashemi, Behzad & Taheri, Shamsodin & Cretu, Ana-Maria & Pouresmaeil, Edris, 2021. "Systematic photovoltaic system power losses calculation and modeling using computational intelligence techniques," Applied Energy, Elsevier, vol. 284(C).
    16. Elbeltagi, Ahmed & Deng, Jinsong & Wang, Ke & Malik, Anurag & Maroufpoor, Saman, 2020. "Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment," Agricultural Water Management, Elsevier, vol. 241(C).
    17. Guilherme Souza & Ricardo Santos & Erlandson Saraiva, 2022. "A Log-Logistic Predictor for Power Generation in Photovoltaic Systems," Energies, MDPI, vol. 15(16), pages 1-16, August.
    18. Ağbulut, Ümit & Gürel, Ali Etem & Sarıdemir, Suat, 2021. "Experimental investigation and prediction of performance and emission responses of a CI engine fuelled with different metal-oxide based nanoparticles–diesel blends using different machine learning alg," Energy, Elsevier, vol. 215(PA).
    19. Mehmet Efe Biresselioglu & Muhittin Hakan Demir, 2022. "Constructing a Decision Tree for Energy Policy Domain Based on Real-Life Data," Energies, MDPI, vol. 15(7), pages 1-15, March.
    20. Yan, Shicheng & Wu, Lifeng & Fan, Junliang & Zhang, Fucang & Zou, Yufeng & Wu, You, 2021. "A novel hybrid WOA-XGB model for estimating daily reference evapotranspiration using local and external meteorological data: Applications in arid and humid regions of China," Agricultural Water Management, Elsevier, vol. 244(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:gam:jsusta:v:16:y:2024:i:9:p:3581-:d:1382075. 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.