A Novel Three-Stage Short-Term Photovoltaic Prediction Approach Based on Neighborhood Component Analysis and ANN Optimized with PSO (NCA-PSO-ANN)

Abstract

AbstractParameters of solar radiation are unstable, which affect the prediction accuracy of photovoltaic (PV) power. Many hybrid models have been applied recently to improve the prediction accuracy of the models. In this chapter, a novel three-stage hybrid model of Neighborhood Component Analysis (NCA), and Artificial Neuronal Network (ANN) optimized with Particle Swarm Optimization (PSO) is introduced. First, the NCA technique is applied as a feature extraction technique to determine the relevant features that have substantial influence on photovoltaic power. The study now applies the chosen feature components as inputs into the optimized ANN with PSO to create the PV prediction model. The prediction strength of the proposed NCA-PSO-ANN model is compared with other variant hybrid models such as Principal Component Analysis (PCA), PSO and ANN (PCA-PSO-ANN), PSO-ANN, and PCA-ANN. The proposed NCA-PSO-ANN model constitutes a very reliable computational tool as it performed better in the selected performance indexes than the compared models.KeywordsNeighborhood Component AnalysisArtificial neural networkDimensionality reductionshort-term forecasting