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Palabras clave – provistas por la editorial

sensor network; data fusion; complex network analysis; fault prognosis; photovoltaic plants; ANFIS; statistical method; gradient descent; photovoltaic system; sustainable development; PV power prediction; artificial neural network; renewable energy; environmental parameters; multiple regression model; moth-flame optimization; parameter extraction; photovoltaic model; double flames generation (DFG) strategy; Solar cell parameters; single-diode model; two-diode model; COA; photovoltaic systems; maximum power point tracking; single stage grid connected systems; solar concentrator; spectral beam splitting; diffractive optical element; diffractive grating; PVs power output forecasting; adaptive neuro-fuzzy inference systems; particle swarm optimization-artificial neural networks; solar irradiation; photovoltaic power prediction; publicly available weather reports; machine learning; long short-term memory; integrated energy systems; smart energy management; PV fleet; clustering-based PV fault detection; unsupervised learning; self-imputation; implicit model solution; photovoltaic array; series–parallel; global optimization; partial shading; deterministic optimization algorithm; metaheuristic optimization algorithm; genetic algorithm; solar cell optimization; finite difference time domain; optical modelling; thermal image; photovoltaic module; hot spot; image processing; deterioration; linear approximation; MPPT algorithm; duty cycle; global horizontal irradiance; mathematical modeling; feed-forward neural networks; recurrent neural networks; LSTM cell; performances evaluation; clear sky irradiance; persistent predictor; photovoltaics; artificial neural networks; national power system