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Solar Radiation Estimation Algorithm and Field Verification in Taiwan

Author

Listed:
  • Ping-Huan Kuo

    (Computer and Intelligent Robot Program for Bachelor Degree, National Pingtung University, Pingtung 90004, Taiwan)

  • Hsin-Chuan Chen

    (School of Computer Engineering, University of Electronic Science and Technology of China Zhongshan Institute, Zhongshan 528402, China)

  • Chiou-Jye Huang

    (School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China)

Abstract

The power generation potential of a solar photovoltaic (PV) power generation system is closely related to the on-site solar radiation, and sunshine conditions are an important reference index for evaluating the installation of a solar PV system. Meanwhile, the long-term operation and maintenance of a PV system needs solar radiation information as a reference for system performance evaluation. Obtaining solar radiation information through the installation of irradiation monitoring stations is often very costly, and the cost of sustaining the reliability of the monitoring system, Internet stability and subsequent operation and maintenance can often be alarming. Therefore, the establishment of a solar radiation estimation model can reduce the installation of monitoring stations and decrease the cost of obtaining solar radiation information. In this study, we use an inverse distance weighting algorithm to establish the solar radiation estimation model. The model was built by obtaining information from 20 solar radiation monitoring stations in central and southern Taiwan, and field verification was implemented at Yuan Chang Township town hall and the Tainan Liujia campus. Furthermore, a full comparison between Inverse Distance Weighting (IDW) and the Kriging method is also given in this paper. The estimation results demonstrate the performance of the IDW method. In the experiment, the performance of the IDW method is better than the Ordinary Kriging (OK) method. The Mean Absolute Percentage Error (MAPE) values of the solar radiation estimation model by IDW at the two field verifications were 4.30% and 3.71%, respectively.

Suggested Citation

  • Ping-Huan Kuo & Hsin-Chuan Chen & Chiou-Jye Huang, 2018. "Solar Radiation Estimation Algorithm and Field Verification in Taiwan," Energies, MDPI, vol. 11(6), pages 1-12, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1374-:d:149433
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    References listed on IDEAS

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    3. Yeji Lee & Doosung Choi & Yongho Jung & Myeongjin Ko, 2022. "Application of Technology to Develop a Framework for Predicting Power Output of a PV System Based on a Spatial Interpolation Technique: A Case Study in South Korea," Energies, MDPI, vol. 15(22), pages 1-22, November.
    4. Olubayo M. Babatunde & Josiah L. Munda & Yskandar Hamam, 2020. "Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation," Energies, MDPI, vol. 13(10), pages 1-18, May.
    5. Alberto Bocca & Luca Bergamasco & Matteo Fasano & Lorenzo Bottaccioli & Eliodoro Chiavazzo & Alberto Macii & Pietro Asinari, 2018. "Multiple-Regression Method for Fast Estimation of Solar Irradiation and Photovoltaic Energy Potentials over Europe and Africa," Energies, MDPI, vol. 11(12), pages 1-17, December.

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