Research Article

Deep Learning Based on Wireless Remote Sensing Model for Monitoring the Solar System Inverter

Table 3

Performance related to PV solar cells derived from the simulation system.

 Yr (kWh/m2.day)LuYu (kWh/kWp/d)LcYa (kWh/kWp/d)LsYf (kWh/kWp/d)PR

January4.470.0004.470.4114.060.0823.980.890
February5.470.0005.470.6244.850.5064.340.794
March5.950.1895.951.0444.900.5614.340.730
April6.320.5896.321.6134.710.3644.340.687
May6.870.7956.872.1594.710.3704.340.632
June7.720.9107.272.5504.720.3744.340.598
July7.100.6927.102.3894.710.3684.340.612
August7.200.7667.202.4974.710.3634.340.603
September6.920.7096.922.2064.710.3674.340.628
October6.190.3316.191.4734.710.3684.340.702
November5.090.0125.090.6694.420.0784.340.853
December4.180.0004.180.4173.760.1183.650.872
Year6.090.4186.091.5094.580.3254.250.699

Yr: reference incident energy in coll.; Lu: normalized unused (full battery); Yu: normalized potential production; Lc: normalized array losses; Ya: normalized array production; Ls: normalized system losses; Yf: normalized system production; PR: performance ratio.