A hybrid neural network based solar PV power classification with time series data for bend, Oregon

Photovoltaic (PV) power classification model has become a significant alternative to PV power point forecasting model in which classes of future PV powers are estimated. In this work, a novel PV power classification model which could be realized using feed forward neural network (FFNN) trained with a hybrid meta-heuristic approach is proposed to forecast the classes of future PV powers. The insisted classification model is implemented to classify three classes of PV power for the location Oregon city of United States of America (USA) with latitude${43.8041<^>0}N$43.80410N, longitude ${120.5542<^>0}W$120.55420W, and altitude of $1124$1124 m. The meta-heuristic approach comprising of Levy Flight (Levy)-Sine Cosine Algorithm (SCA)-Particle Swarm Optimization (PSO) is utilized for training the parameters of FFNN by minimizing the root mean square error$\left({{\rm{RMSE}}} \right)$RMSE. The results disclose that the proposed approach attained the most accurate prediction of PV power with $\% {\rm{MAPE}} = 2.38$%MAPE=2.38 for the month of November as well as high classification accuracy with ${\rm{PCA}} = 94.96\% $PCA=94.96% in the month of April. Based on the obtained results, the proposed LF-SCA-PSO trained FFNN outperformed the existing hybrid models in both prediction and classification and hence possess the potential to be a new alternative to assist engineers in predicting the PV power of solar systems at short- and long-time horizons.