Prediction of photovoltaic system output using hybrid Cuckoo Search Least Square Support Vector Machine / Muhammad Aidil Adha Aziz

The electrical system photovoltaic (PV) modules for special design considerations due to unpredictable and sudden changes in weather conditions such as the solar irradiation level as well as the cell operating temperature. This thesis presents a practical and reliable approach for the prediction of PV power output using an intelligent-based technique namely Cuckoo Search Algorithm - Least Square Support Vector Machine (CS-LSSVM). Available historical output power data are analyzed and appropriate features are selected for the model. There are two input vectors to the model consist of solar irradiation and ambient temperature. Therefore, Cuckoo Search Algorithm (CS) is hybrid with LS-SVM in order to optimize the RBF parameters for a better prediction performance. The CS algorithm is inspired by the life of a bird family, called Cuckoo. This algorithm imitated from the effort of the cuckoos to survive. The performance of CS-LSSVM is compared with those obtained from LS-SVM using cross-validation technique in terms of accuracy. In this thesis, Mean Absolute Percentage Error (MAPE) is used to quantify the performance of the prediction. Besides that, evaluation also carried out by calculating the correlation of determination. The historical PV data is utilized to validate the workability of the proposed technique. The results showed that CS-LSSVM provides better performance in predicting photovoltaic system power output as compared to conventional LS-SVM using cross-validation technique.