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Artificial Intelligence Applications in Estimating Invisible Solar Power Generation

Author

Listed:
  • Yuan-Kang Wu

    (Department of Electrical Engineering, National Chung-Cheng University, Chia-Yi 62102, Taiwan)

  • Yi-Hui Lai

    (Department of Electrical Engineering, National Chung-Cheng University, Chia-Yi 62102, Taiwan)

  • Cheng-Liang Huang

    (Department of Electrical Engineering, National Chung-Cheng University, Chia-Yi 62102, Taiwan)

  • Nguyen Thi Bich Phuong

    (Department of Electrical Engineering, National Chung-Cheng University, Chia-Yi 62102, Taiwan)

  • Wen-Shan Tan

    (School of Engineering and Advance Engineering Platform, Monash University Malaysia, Subang Jaya 47500, Selangor, Malaysia)

Abstract

In recent years, the penetration of photovoltaic (PV) power generation in Taiwan has increased significantly. However, most photovoltaic facilities, especially for small-scale sites, do not include relevant monitoring and real-time measurement devices. The invisible power generation from these PV sites would cause a huge challenge on power system scheduling. Therefore, appropriate methods to estimate invisible PV power generation are needed. The main purpose of this paper is to propose an improved fuzzy model for estimating the PV power generation, which includes the clustering processing for PV sites, selection of representative PV sites, and the improvement of the conventional fuzzy model. First, this research uses the K-nearest neighbor (KNN) algorithm to fill in some of the missing data; then, two clustering algorithms are applied to cluster all the photovoltaic sites. Next, the relationship between the power generation of a single PV site and the total generation of all sites at the same cluster is further analyzed to select the representative PV sites. Finally, an improved fuzzy model is implemented to estimate the PV power generation. This research used actual data that were measured from PV sites in Taiwan for the estimation, verification, and comparison study. The numerical results demonstrate that the proposed method can obtain an average estimation error about 7% by using limit measurements from PV sites, highlighting the high efficiency and practicability of the proposed method.

Suggested Citation

  • Yuan-Kang Wu & Yi-Hui Lai & Cheng-Liang Huang & Nguyen Thi Bich Phuong & Wen-Shan Tan, 2022. "Artificial Intelligence Applications in Estimating Invisible Solar Power Generation," Energies, MDPI, vol. 15(4), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1312-:d:747190
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    References listed on IDEAS

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    1. Li, Kangping & Wang, Fei & Mi, Zengqiang & Fotuhi-Firuzabad, Mahmoud & Duić, Neven & Wang, Tieqiang, 2019. "Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Fei Wang & Kangping Li & Xinkang Wang & Lihui Jiang & Jianguo Ren & Zengqiang Mi & Miadreza Shafie-khah & João P. S. Catalão, 2018. "A Distributed PV System Capacity Estimation Approach Based on Support Vector Machine with Customer Net Load Curve Features," Energies, MDPI, vol. 11(7), pages 1-19, July.
    3. Stainsby, Wendell & Zimmerle, Daniel & Duggan, Gerald P., 2020. "A method to estimate residential PV generation from net-metered load data and system install date," Applied Energy, Elsevier, vol. 267(C).
    4. Shaker, Hamid & Manfre, Daniel & Zareipour, Hamidreza, 2020. "Forecasting the aggregated output of a large fleet of small behind-the-meter solar photovoltaic sites," Renewable Energy, Elsevier, vol. 147(P1), pages 1861-1869.
    5. Mingoti, Sueli A. & Lima, Joab O., 2006. "Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1742-1759, November.
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    Cited by:

    1. Ganapathy Ramesh & Jaganathan Logeshwaran & Thangavel Kiruthiga & Jaime Lloret, 2023. "Prediction of Energy Production Level in Large PV Plants through AUTO-Encoder Based Neural-Network (AUTO-NN) with Restricted Boltzmann Feature Extraction," Future Internet, MDPI, vol. 15(2), pages 1-20, January.
    2. Andi A. H. Lateko & Hong-Tzer Yang & Chao-Ming Huang, 2022. "Short-Term PV Power Forecasting Using a Regression-Based Ensemble Method," Energies, MDPI, vol. 15(11), pages 1-21, June.
    3. Guodong Liu & Thomas B. Ollis & Maximiliano F. Ferrari & Aditya Sundararajan & Kevin Tomsovic, 2022. "Robust Scheduling of Networked Microgrids for Economics and Resilience Improvement," Energies, MDPI, vol. 15(6), pages 1-19, March.

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