Development of Methodology for the Evaluation of Solar Energy through Hybrid Models for the Energy Sector
Development of Methodology for the Evaluation of Solar Energy through Hybrid Models for the Energy Sector
The forecast of the generation of electrical energy from the solar resource is associated with its uncertainty due to the meteorological variations that it presents. Solar power generation forecasts are important for the efficient operation of solar plants. This article shows a methodology entailing a multilayer neural network with backpropagation and input data from a model with time lag coordinates for a horizon of 24 h and beyond. The neural network model was compared with statistical and prediction models numerical time, resulting in a MAPE of 0.57% and a MAE of 69.29 W.
power energy, Engineering machinery, tools, and implements, neural network, forecasting, TA213-215
power energy, Engineering machinery, tools, and implements, neural network, forecasting, TA213-215
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