TY - JOUR
T1 - Artificial Intelligence applications in estimating invisible solar power generation
AU - Wu, Yuan-Kang
AU - Lai, Yi-Hui
AU - Huang, Cheng-Liang
AU - Phuong, Nguyen Thi Bich
AU - Tan, Wen-Shan
N1 - Funding Information:
Acknowledgments: This work is financially supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109‐2622‐E‐194 ‐005.
Funding Information:
Funding: This research was funded by the Ministry of Science and Technology (MOST) of Taiwan, grant number MOST 109‐2622‐E‐194 ‐005.
Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/2/2
Y1 - 2022/2/2
N2 - 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.
AB - 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.
KW - Fuzzy systems
KW - Invisible power generation
KW - Power estimation
KW - Representative PV sites
KW - Solar photovoltaic
UR - http://www.scopus.com/inward/record.url?scp=85124600116&partnerID=8YFLogxK
U2 - 10.3390/en15041312
DO - 10.3390/en15041312
M3 - Article
AN - SCOPUS:85124600116
SN - 1996-1073
VL - 15
JO - Energies
JF - Energies
IS - 4
M1 - 1312
ER -