Electrical Characteristics Estimation of Photovoltaic Modules via Cuckoo Search—Relevant Vector Machine Probabilistic Model - Ban, Jianmin; Pan, Xinyu; Gu, Minming (Frontiers in Energy Research, 2021)
This work presents an optimized probabilistic modeling methodology that facilitates the modeling of photovoltaic (PV) modules with measured data over a range of environmental conditions. The method applies cuckoo search to optimize kernel parameters, followed by electrical characteristics estimation via relevance vector machine. Unlike analytical modeling techniques, the proposed cuckoo search-relevance vector machine (CS-RVM) takes advantages of no required knowledge of internal PV parameters, more accurate estimation capability and less computational effort. A comparative study has been done among the electrical characteristics predicted by back-propagation neural network (BPNN), radial basis function neural network (RBFNN), support vector machine (SVM), Villalva's model, relevance vector machine (RVM) and the CS-RVM. Experimental results show that the proposed CS-RVM provides the best prediction in most scenarios.