As for the proposed technology by combining the artificial bee colony algorithmthe semi-supervised extreme learning machine (ABC-SSELM), characteristic parameter normalization equations of I-V curves are tuned via low-cost data under normal operation of PV strings. The proposed ABC-SSELM method only needs 1-3 labeled data of the total dataset to save humantime cost. The accuracy of diagnosing various mixed faults can reach more than 99.84,the monitoring of dust accumulation can provide effective cleaning to increase the revenue of the solar PV power generation system.

Our technique combines the use of LOD2TMY3 datatake metropolitan area as the case study. We demonstrated that accurate rooftop solar power potential can be approached for solar power developing evaluation by importing solar irradiation, inclination, shadow covering information based on simulated but accurate building environment. Our technique enables a scientific approach for supporting future solar energy planningdevelopments.