IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i13p7945-d851636.html
   My bibliography  Save this article

Evaluation of Artificial Neural Networks with Satellite Data Inputs for Daily, Monthly, and Yearly Solar Irradiation Prediction for Pakistan

Author

Listed:
  • Faisal Nawab

    (Solar Energy Research Institute, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
    Department of Renewable Energy, Khushal Khan Khattak University, Karak 27200, Pakistan)

  • Ag Sufiyan Abd Hamid

    (Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Kota Kinabalu 88400, Malaysia)

  • Ali Alwaeli

    (Engineering Department, American University of Iraq, Sulaimani, Kurdistan Region, Sulaimani 46001, Iraq)

  • Muhammad Arif

    (Center for Advanced Studies in Energy, University of Engineering and Technology, Peshawar 25120, Pakistan)

  • Mohd Faizal Fauzan

    (Solar Energy Research Institute, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia)

  • Adnan Ibrahim

    (Solar Energy Research Institute, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia)

Abstract

Solar irradiation is the most critical parameter to consider when designing solar energy systems. The high cost and difficulty of measuring solar irradiation makes it impractical in every location. This study’s primary objective was to develop an artificial neural network (ANN) model for global horizontal irradiation (GHI) prediction using satellite data inputs. Three types of ANN, namely, the feed forward neural network (FFNN), cascaded forward neural network (CFNN), and Elman neural network (EMNN), were tested. The findings revealed that altitude, relative humidity, and satellite GHI are the most effective parameters, as they are present in all the best-performing models. The best model for daily GHI prediction was FFNN, which decreased daily MAPE, RMSE, and MBE by 25.4%, 0.11 kWh/ m 2 / d , and 0.01 kWh/ m 2 / d . The FFNN daily MAPE, RMSE, and MBE values were 7.83%, 0.49 kWh/ m 2 / d , and 0.01 kWh/m 2 /d. The EMNN performed best for monthly and annual prediction, reducing monthly MAPE, RMSE, and MBE by 50.62%, 0.13 kWh/ m 2 / d , and 0.13 kWh/ m 2 / d , while the reduction for yearly was 91.6%, 0.11 kWh/ m 2 / d , 0.2 kWh/ m 2 / d . The EMNN monthly MAPE, RMSE, and MBE values were 3.36%, 0.16 kWh/ m 2 / d , and 0.16 kWh/ m 2 / d , while the yearly values were 0.47%, 0.18 kWh/ m 2 / d , and 0.004 kWh/ m 2 / d .

Suggested Citation

  • Faisal Nawab & Ag Sufiyan Abd Hamid & Ali Alwaeli & Muhammad Arif & Mohd Faizal Fauzan & Adnan Ibrahim, 2022. "Evaluation of Artificial Neural Networks with Satellite Data Inputs for Daily, Monthly, and Yearly Solar Irradiation Prediction for Pakistan," Sustainability, MDPI, vol. 14(13), pages 1-20, June.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7945-:d:851636
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/13/7945/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/13/7945/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mubiru, J., 2008. "Predicting total solar irradiation values using artificial neural networks," Renewable Energy, Elsevier, vol. 33(10), pages 2329-2332.
    2. Sözen, Adnan & Arcaklioglu, Erol & Özalp, Mehmet & Kanit, E. Galip, 2004. "Use of artificial neural networks for mapping of solar potential in Turkey," Applied Energy, Elsevier, vol. 77(3), pages 273-286, March.
    3. Toghraie, Davood & Sina, Nima & Jolfaei, Niyusha Adavoodi & Hajian, Mehdi & Afrand, Masoud, 2019. "Designing an Artificial Neural Network (ANN) to predict the viscosity of Silver/Ethylene glycol nanofluid at different temperatures and volume fraction of nanoparticles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    4. Ahmed Aljanad & Nadia M. L. Tan & Vassilios G. Agelidis & Hussain Shareef, 2021. "Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 14(4), pages 1-20, February.
    5. Senkal, Ozan & Kuleli, Tuncay, 2009. "Estimation of solar radiation over Turkey using artificial neural network and satellite data," Applied Energy, Elsevier, vol. 86(7-8), pages 1222-1228, July.
    6. Kaba, Kazım & Sarıgül, Mehmet & Avcı, Mutlu & Kandırmaz, H. Mustafa, 2018. "Estimation of daily global solar radiation using deep learning model," Energy, Elsevier, vol. 162(C), pages 126-135.
    7. Tahir, Zia ul Rehman & Azhar, Muhammad & Blanc, Philippe & Asim, Muhammad & Imran, Shahid & Hayat, Nasir & Shahid, Hamza & Ali, Hasnain, 2020. "The evaluation of reanalysis and analysis products of solar radiation for Sindh province, Pakistan," Renewable Energy, Elsevier, vol. 145(C), pages 347-362.
    8. Tahir, Z.R. & Asim, Muhammad, 2018. "Surface measured solar radiation data and solar energy resource assessment of Pakistan: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2839-2861.
    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. Sergio Celada-Bernal & Guillermo Pérez-Acosta & Carlos M. Travieso-González & José Blanco-López & Luciano Santana-Cabrera, 2023. "Applying Neural Networks to Recover Values of Monitoring Parameters for COVID-19 Patients in the ICU," Mathematics, MDPI, vol. 11(15), pages 1-19, July.
    2. Elias Roumpakias & Tassos Stamatelos, 2023. "Comparative Performance Analysis of a Grid-Connected Photovoltaic Plant in Central Greece after Several Years of Operation Using Neural Networks," Sustainability, MDPI, vol. 15(10), pages 1-26, May.
    3. Faisal Nawab & Ag Sufiyan Abd Hamid & Muhammad Arif & Tufial A. Khan & Amir Naveed & Muhammad Sadiq & Sahibzada Imad Ud din & Adnan Ibrahim, 2022. "Solar–Biogas Microgrid: A Strategy for the Sustainable Development of Rural Communities in Pakistan," Sustainability, MDPI, vol. 14(18), pages 1-15, September.

    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. Linares-Rodríguez, Alvaro & Ruiz-Arias, José Antonio & Pozo-Vázquez, David & Tovar-Pescador, Joaquín, 2011. "Generation of synthetic daily global solar radiation data based on ERA-Interim reanalysis and artificial neural networks," Energy, Elsevier, vol. 36(8), pages 5356-5365.
    2. Deo, Ravinesh C. & Wen, Xiaohu & Qi, Feng, 2016. "A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset," Applied Energy, Elsevier, vol. 168(C), pages 568-593.
    3. Kılıç, Fatih & Yılmaz, İbrahim Halil & Kaya, Özge, 2021. "Adaptive co-optimization of artificial neural networks using evolutionary algorithm for global radiation forecasting," Renewable Energy, Elsevier, vol. 171(C), pages 176-190.
    4. Makade, Rahul G. & Chakrabarti, Siddharth & Jamil, Basharat & Sakhale, C.N., 2020. "Estimation of global solar radiation for the tropical wet climatic region of India: A theory of experimentation approach," Renewable Energy, Elsevier, vol. 146(C), pages 2044-2059.
    5. 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.
    6. Yadav, Amit Kumar & Malik, Hasmat & Chandel, S.S., 2014. "Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 509-519.
    7. Heo, Jae & Jung, Jaehoon & Kim, Byungil & Han, SangUk, 2020. "Digital elevation model-based convolutional neural network modeling for searching of high solar energy regions," Applied Energy, Elsevier, vol. 262(C).
    8. Janjai, S. & Pankaew, P. & Laksanaboonsong, J., 2009. "A model for calculating hourly global solar radiation from satellite data in the tropics," Applied Energy, Elsevier, vol. 86(9), pages 1450-1457, September.
    9. Işık, Erdem & Inallı, Mustafa, 2018. "Artificial neural networks and adaptive neuro-fuzzy inference systems approaches to forecast the meteorological data for HVAC: The case of cities for Turkey," Energy, Elsevier, vol. 154(C), pages 7-16.
    10. Janjai, Serm & Plaon, Piyanuch, 2011. "Estimation of sky luminance in the tropics using artificial neural networks: Modeling and performance comparison with the CIE model," Applied Energy, Elsevier, vol. 88(3), pages 840-847, March.
    11. Baser, Furkan & Demirhan, Haydar, 2017. "A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation," Energy, Elsevier, vol. 123(C), pages 229-240.
    12. Hejase, Hassan A.N. & Al-Shamisi, Maitha H. & Assi, Ali H., 2014. "Modeling of global horizontal irradiance in the United Arab Emirates with artificial neural networks," Energy, Elsevier, vol. 77(C), pages 542-552.
    13. Purohit, Ishan & Purohit, Pallav, 2015. "Inter-comparability of solar radiation databases in Indian context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 735-747.
    14. Muhammad Asim & Adnan Qamar & Ammara Kanwal & Ghulam Moeen Uddin & Muhammad Mujtaba Abbas & Muhammad Farooq & M. A. Kalam & Mohamed Mousa & Kiran Shahapurkar, 2022. "Opportunities and Challenges for Renewable Energy Utilization in Pakistan," Sustainability, MDPI, vol. 14(17), pages 1-15, September.
    15. Dahmani, Kahina & Notton, Gilles & Voyant, Cyril & Dizene, Rabah & Nivet, Marie Laure & Paoli, Christophe & Tamas, Wani, 2016. "Multilayer Perceptron approach for estimating 5-min and hourly horizontal global irradiation from exogenous meteorological data in locations without solar measurements," Renewable Energy, Elsevier, vol. 90(C), pages 267-282.
    16. Mehmood, Faiza & Ghani, Muhammad Usman & Asim, Muhammad Nabeel & Shahzadi, Rehab & Mehmood, Aamir & Mahmood, Waqar, 2021. "MPF-Net: A computational multi-regional solar power forecasting framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    17. Preeti Verma & Sunil Patil, 2023. "A Machine Learning Approach and Methodology for Solar Radiation Assessment Using Multispectral Satellite Images," Annals of Data Science, Springer, vol. 10(4), pages 907-932, August.
    18. Kisi, Ozgur, 2014. "Modeling solar radiation of Mediterranean region in Turkey by using fuzzy genetic approach," Energy, Elsevier, vol. 64(C), pages 429-436.
    19. Jawed Mustafa & Shahid Husain & Saeed Alqaed & Uzair Ali Khan & Basharat Jamil, 2022. "Performance of Two Variable Machine Learning Models to Forecast Monthly Mean Diffuse Solar Radiation across India under Various Climate Zones," Energies, MDPI, vol. 15(21), pages 1-32, October.
    20. Cao, Qimeng & Liu, Yan & Sun, Xue & Yang, Liu, 2022. "Country-level evaluation of solar radiation data sets using ground measurements in China," Energy, Elsevier, vol. 241(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:14:y:2022:i:13:p:7945-:d:851636. 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.