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Fig. 3. Measured and predicted values (Jun 6") Fig. 2 describes the prediction for Jun 5". We can see that the trend of solar irradiance almost the same from the previous day. LSSVM predictor accurately forecast the solar irradiance with 96.87% accuracy. In Fig.3 we can see the prediction for the next day based on the previous data. LSSVM predictor successfully forecast the values and gave 97.75% accuracy. From the results obtained, The LSSVM

Figure 3 Measured and predicted values (Jun 6") Fig. 2 describes the prediction for Jun 5". We can see that the trend of solar irradiance almost the same from the previous day. LSSVM predictor accurately forecast the solar irradiance with 96.87% accuracy. In Fig.3 we can see the prediction for the next day based on the previous data. LSSVM predictor successfully forecast the values and gave 97.75% accuracy. From the results obtained, The LSSVM

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GCPV SYSTEM DESCRIPTION LSSVM is an alteration of the standard SVM and was improved by Suykens et al. [39]. LSSVM uses equality constraints instead of inequality constraints and a least squares error term instead of the standard error term. The Grid Connected Photovoltaic (GCPV) power system involved in this research situated in Green Energy Research Center (GERC), University of Technology MARA (UiTM) Shah Alam, Selangor. The system can be described as Table 1. The data of solar irradiance, module temperature, power, voltage and current has been analysed from May 2012 which are recorded every 5 minutes interval using dedicated in- built data logger in the individual grid inverter.
GCPV SYSTEM DESCRIPTION LSSVM is an alteration of the standard SVM and was improved by Suykens et al. [39]. LSSVM uses equality constraints instead of inequality constraints and a least squares error term instead of the standard error term. The Grid Connected Photovoltaic (GCPV) power system involved in this research situated in Green Energy Research Center (GERC), University of Technology MARA (UiTM) Shah Alam, Selangor. The system can be described as Table 1. The data of solar irradiance, module temperature, power, voltage and current has been analysed from May 2012 which are recorded every 5 minutes interval using dedicated in- built data logger in the individual grid inverter.
PARAMETERS VALUES IN SOLAR IRRADIANCE PREDICTORS. TABLE II irradiance and accurately forecast the solar irradiance for the next day with 95.43% of accuracy.
PARAMETERS VALUES IN SOLAR IRRADIANCE PREDICTORS. TABLE II irradiance and accurately forecast the solar irradiance for the next day with 95.43% of accuracy.
Fig. 2. Measured and predicted values (Jun 5")
Fig. 2. Measured and predicted values (Jun 5")
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