Aiming at the problem that the measurement error of power metering eyuipment is difficult to predict under multi-stressconditions, a measurement error prediction method of power metering equipment is proposed based on kernel support veetor regression(KSVR),and an Optimized Genetic Algorithm(OGA) is proposed to optimize the kemel parameters. Firstly, a linear weightedmulti-kermel fumetion is prmposed to fuse multiple stress features, and the kemel weight coefficient is used to deseribe the influence ofdiferent stresses on the power metering equipment. Then, in the parameter selection stage of the kermel function, to avoid the limitationof manual parameter adjustment, an OGA with crossover probability and mutation probability adaptive adjustment is proposed and appliedto the optimization selection issue of kernel parameters. The operation data analysis of the smart electricity meters in Xinjiang High DryHeat Test Base of State Grid shows that the proposed model has high aceuraey, the average mean square error of the prediction results is 0.00018,and the highest goodness of fit can reach 0.989, which can provide a targeted strategy for the health management of powermetering equipment under multiple environmental stresscs.