Artificial Intelligence Applications in Estimating Invisible Solar Power Generation

Yuan-Kang Wu, Yi-Hui Lai, Cheng-Liang Huang, Nguyen Thi Bich Phuong and Wen-Shan Tan
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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

Energies, 2022, vol. 15, issue 4, 1-18

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.

Keywords: solar photovoltaic; invisible power generation; representative PV sites; power estimation; fuzzy systems (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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