K-Means Clustering

Photovoltaic (PV) systems have gained global acceptance in terms of green, replenishable energy resources to meet energy demand with no emissions. However, PV systems are susceptible to operational and environmental stresses. Moreover, due to their no supervisory control, PV panels monitoring is necessary to keep its performance and efficiency intact. Therefore, this study monitors PV panels based on health into three sub-classes: healthy, hotspot, and faulty through infrared thermography. Thermographs key points are selected using an 8x8 uniform pixel grid and speed-up robust features (SURF) are extracted from grid intersection points. Afterwards, k-mean clustering algorithm due to its simplicity, creating single level clusters based on actual observations similarities and similar observations closeness within cluster and dissimilarity to other clusters observations, is used to transform features into visual words. Finally, shallow classifiers because of low training time and high prediction speed are utilized. After extensive testing and compressive analysis, the proposed approach was found economical, fast, and showed high testing accuracy of 97% through a multi-class shallow classifier (support vector machine) with low computational complexity and less storage size. Thus, this approach can be employed to monitor mega-watt PV systems with high accuracy and to keep performance and emissions mitigation potential high while lowering payback time.

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Dynamics of precipitation regions of Turkey: A clustering approach by K-means methodology in respect of climate variability

Kömüşçü

Turgu²,

DeLiberty

2022

Journal of Water and Climate Change

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Delineation of homogenous precipitation regions can be rather challenging when considerable spatial variability and seasonality of precipitation exist within a large geographic domain. This study aims to investigate and redefine the spatio-temporal variability of precipitation clusters of Turkey by K-means clustering at different time scales. The monthly precipitation of 234 stations for the 1980–2020 period was included. Five precipitation clusters are identified for the 1980–2020 period, while the clusters varied between four and eight and four and six for the 5-year and 10-year sub-periods, respectively. The clustering structures exhibited indicated certain links to the prominent teleconnection patterns. The monthly precipitation correlated more significantly with Arctic Oscillation (AO) in the Aegean and Mediterranean regions, while North Atlantic Oscillation correlated better with the Eastern and Southeastern Anatolian stations. The variability of the cluster structures is described in association with the coefficient of variation (COV) spatial patterns using the observed station data. At 5-year and 10-year subsequent periods, no significant variability in the cluster patterns was observed in the lower COV areas where the monthly precipitation was more uniform throughout the year, while more inter-decadal variability was prominent in the higher COV areas where seasonality in precipitation was more pronounced. Overall, the precipitation regions were not spatially coherent over time, and considerable variability was revealed between different regions of the country.

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K-Means Clustering

Cited by 4 publications

References 7 publications

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Dynamics of precipitation regions of Turkey: A clustering approach by K-means methodology in respect of climate variability

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