Development of Methodology for the Evaluation of Solar Energy through Hybrid Models for the Energy Sector

Engineering Proceedings

article

Georgina González-González¹  Jesús Cerezo-Román¹  Guillermo Satamaría-Bonfil³ 

[1] Center for Engineering and Applied Sciences, Autonomous University of State Morelos;Laboratory Technician School, Autonomous University of State Morelos;Data Portfolio Manager Department

关键词: forecasting;    power energy;    neural network;   

DOI  :  10.3390/engproc2023039073

来源: mdpi

【 摘 要 】

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.

【 授权许可】

Unknown