IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v222y2024ics096014812400082x.html
   My bibliography  Save this article

Simulation and forecasting of power by energy harvesting method in photovoltaic panels using artificial neural network

Author

Listed:
  • Demir, Hasan

Abstract

Heat is an important efficiency reducing factor in photovoltaic systems. Although many studies in the literature are related to the removal of waste heat from photovoltaic panels, the disadvantages such as installation and maintenance of cooling systems should not be forgotten. A study has shown that with a new approach, energy can be produced with the method of energy harvesting from waste heat. The results of the study were limited to a geographical region and climatic conditions in Aksaray, Turkey. In this article, the results of the study, which was limited to a region, were extended using the finite element method analysis and the artificial neural network model. Ten different cases were determined and temperature gradient was found by finite element analysis. The forecasting algorithm was developed with artificial neural network and estimates the harvestable power based on the temperature gradient. The accuracy of the algorithm was tested with the MSE and nRMSE statistical metrics which were calculated as 59.1423 mW and 13.6189 %, respectively. The training data accuracy of the network was 0.93987 and the combined accuracy was 0.94364. The results of this study are important to be a reference for researchers who want to establish a photovoltaic panel energy harvesting system.

Suggested Citation

  • Demir, Hasan, 2024. "Simulation and forecasting of power by energy harvesting method in photovoltaic panels using artificial neural network," Renewable Energy, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:renene:v:222:y:2024:i:c:s096014812400082x
    DOI: 10.1016/j.renene.2024.120017
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096014812400082X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2024.120017?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. Cha, Youngsu & Chae, Woojin & Kim, Hubert & Walcott, Horace & Peterson, Sean D. & Porfiri, Maurizio, 2016. "Energy harvesting from a piezoelectric biomimetic fish tail," Renewable Energy, Elsevier, vol. 86(C), pages 449-458.
    2. Burmistrov, Igor & Gorshkov, Nikolay & Kovyneva, Natalya & Kolesnikov, Evgeny & Khaidarov, Bekzod & Karunakaran, Gopalu & Cho, Eun-Bum & Kiselev, Nikolay & Artyukhov, Denis & Kuznetsov, Denis & Gorokh, 2020. "High seebeck coefficient thermo-electrochemical cell using nickel hollow microspheres electrodes," Renewable Energy, Elsevier, vol. 157(C), pages 1-8.
    3. 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.
    4. Yayla, Sedat & Ayça, Sümeyya & Oruç, Mehmet, 2020. "A case study on piezoelectric energy harvesting with using vortex generator plate modeling for fluids," Renewable Energy, Elsevier, vol. 157(C), pages 1243-1253.
    5. Harb, Adnan, 2011. "Energy harvesting: State-of-the-art," Renewable Energy, Elsevier, vol. 36(10), pages 2641-2654.
    6. Hasan Demir, 2022. "Application of Thermal Energy Harvesting from Photovoltaic Panels," Energies, MDPI, vol. 15(21), pages 1-12, November.
    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. Qian, Feng & Liu, Mingyi & Huang, Jianuo & Zhang, Jiajun & Jung, Hyunjun & Deng, Zhiqun Daniel & Hajj, Muhammad R. & Zuo, Lei, 2022. "Bio-inspired bistable piezoelectric energy harvester for powering animal telemetry tags: Conceptual design and preliminary experimental validation," Renewable Energy, Elsevier, vol. 187(C), pages 34-43.
    2. Motora, Kebena Gebeyehu & Wu, Chang-Mou & Rani, Gokana Mohana & Yen, Wan-Tzu & Lin, Kai-Shiang, 2023. "Effect of electrode patterns on piezoelectric energy harvesting property of zinc oxide polyvinylidene fluoride based piezoelectric nanogenerator," Renewable Energy, Elsevier, vol. 217(C).
    3. Li, Zhongjie & Zhao, Li & Wang, Junlei & Yang, Zhengbao & Peng, Yan & Xie, Shaorong & Ding, Jiheng, 2023. "Piezoelectric energy harvesting from extremely low-frequency vibrations via gravity induced self-excited resonance," Renewable Energy, Elsevier, vol. 204(C), pages 546-555.
    4. 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.
    5. Trigo-González, Mauricio & Batlles, F.J. & Alonso-Montesinos, Joaquín & Ferrada, Pablo & del Sagrado, J. & Martínez-Durbán, M. & Cortés, Marcelo & Portillo, Carlos & Marzo, Aitor, 2019. "Hourly PV production estimation by means of an exportable multiple linear regression model," Renewable Energy, Elsevier, vol. 135(C), pages 303-312.
    6. Pedro, Hugo T.C. & Lim, Edwin & Coimbra, Carlos F.M., 2018. "A database infrastructure to implement real-time solar and wind power generation intra-hour forecasts," Renewable Energy, Elsevier, vol. 123(C), pages 513-525.
    7. Agga, Ali & Abbou, Ahmed & Labbadi, Moussa & El Houm, Yassine, 2021. "Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models," Renewable Energy, Elsevier, vol. 177(C), pages 101-112.
    8. Mousavi, Navid & Kothapalli, Ganesh & Habibi, Daryoush & Das, Choton K. & Baniasadi, Ali, 2020. "A novel photovoltaic-pumped hydro storage microgrid applicable to rural areas," Applied Energy, Elsevier, vol. 262(C).
    9. Wang, Zhenyu & Zhang, Yunpeng & Li, Guorong & Zhang, Jinlong & Zhou, Hai & Wu, Ji, 2024. "A novel solar irradiance forecasting method based on multi-physical process of atmosphere optics and LSTM-BP model," Renewable Energy, Elsevier, vol. 226(C).
    10. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    11. Stephan Schlüter & Fabian Menz & Milena Kojić & Petar Mitić & Aida Hanić, 2022. "A Novel Approach to Generate Hourly Photovoltaic Power Scenarios," Sustainability, MDPI, vol. 14(8), pages 1-16, April.
    12. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    13. Edna S. Solano & Payman Dehghanian & Carolina M. Affonso, 2022. "Solar Radiation Forecasting Using Machine Learning and Ensemble Feature Selection," Energies, MDPI, vol. 15(19), pages 1-18, September.
    14. Ke Yan & Xudong Wang & Yang Du & Ning Jin & Haichao Huang & Hangxia Zhou, 2018. "Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy," Energies, MDPI, vol. 11(11), pages 1-15, November.
    15. Neethu Elizabeth Michael & Manohar Mishra & Shazia Hasan & Ahmed Al-Durra, 2022. "Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique," Energies, MDPI, vol. 15(6), pages 1-20, March.
    16. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "A Green Energy Application in Energy Management Systems by an Artificial Intelligence-Based Solar Radiation Forecasting Model," Energies, MDPI, vol. 11(4), pages 1-15, April.
    17. 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.
    18. Kanwal, S. & Khan, B. & Ali, S.M. & Mehmood, C.A., 2018. "Gaussian process regression based inertia emulation and reserve estimation for grid interfaced photovoltaic system," Renewable Energy, Elsevier, vol. 126(C), pages 865-875.
    19. Zhao, He & Huang, Xiaoqiao & Xiao, Zenan & Shi, Haoyuan & Li, Chengli & Tai, Yonghang, 2024. "Week-ahead hourly solar irradiation forecasting method based on ICEEMDAN and TimesNet networks," Renewable Energy, Elsevier, vol. 220(C).
    20. Igor Burmistrov & Rita Khanna & Nikolay Gorshkov & Nikolay Kiselev & Denis Artyukhov & Elena Boychenko & Andrey Yudin & Yuri Konyukhov & Maksim Kravchenko & Alexander Gorokhovsky & Denis Kuznetsov, 2022. "Advances in Thermo-Electrochemical (TEC) Cell Performances for Harvesting Low-Grade Heat Energy: A Review," Sustainability, MDPI, vol. 14(15), pages 1-17, August.

    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:222:y:2024:i:c:s096014812400082x. 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.