| dc.description.abstract | Information is useless if it is not trimmed, sorted, or organized in nowadays the network era. Converting tacit information with potential value to explicit knowledge is not only a process of data management, but also the spirits of data mining technologies. Owing to requiring direct normal irradiance (DNI), the concentration photovoltaic (CPV) system comprises hundreds of solar trackers to carry modules to trace the sun. General speaking, possible reasons for the output power of the CPV system going down are deteriorating or failed components, adjustment problems, or under influences of environmental factors, such as: dirt, pollen, or soiling. However, maintenance methods and strategies are varied among those reasons, i.e., system will be recovered by repairing the deteriorated, failed, or making an adjustment, but to resume the output power from soiling depends on the sites, seasons, and rains. In this study, via regression technology, authors exploited the influences on CPV system by soiling or dirt; also, through R-square, to understand the trend after raining or module cleaning. In the meantime, comprehending influences on modules are varied in terms of environmental factors on different locations. In the end, a cleaning decision making strategy is promoted to save manpower, deduct resource waste, achieve cost reduction purpose, enhance system availability, and secure the best overall benefits of CPV systems.
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