An Intelligent Maintenance and Repair Informing System Using Machine Learning Techniques for Solar Power Generation Equipment

Abstract

This paper proposes an intelligent maintenance and repair informing system for solar power generation equipment, which the machine-learning techniques adopted to improve the time for manually judging the abnormal operation of the equipment. Thereby the maintenance efficiency of the equipment can be enhanced to reduce solar power generation losses. Based on the current environmental sensing data and power generation data of equipment, the proposed system infers whether the status of the solar power generation of each equipment is abnormal and its possible causes. Moreover, the maintenance and repair informing procedures can be processed and decided within 15 minutes.