Solar Irradiance Forecasting in Tropical Weather using an Evolutionary Lean Neural Network

Foo, Yong Wee; Goh, Cindy

doi:10.1109/cec45853.2021.9504875

Cited by 2 publications

(1 citation statement)

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“…Foo and Goh[50] use an evolutionary lean neural network to forecast solar irradiance in tropical weather conditions. In[51] authors use several machine learning (ML) algorithms, including MLP, LSTM, CNN, and hybrid models, in South Africa for a time interval of up to one hour.…”

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confidence: 99%

Neural Networks Forecast Models Comparison for the Solar Energy Generation in Amazon Basin

Marques,

Teixeira,

De Almeida

et al. 2024

IEEE Access

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Deep learning has grown among the prediction tools used within renewable energy options. Solar energy belongs to the options with the lowest atmosphere impact after considering their limitations. In the last five years, Brazil has seen the expansion of wind and solar options almost all over the country, and to preserve the Amazon rainforest, the use of solar energy has helped large and small cities towards a greener future. The novelty of this research covers the use of Deep Learning with data from twelve cities in the state of Amazonas to forecast solar irradiation (W.h/m 2 ) within 30 days. The data input came from ground stations, as much as possible, and NASA satellite models, with a daily time aggregation. The types of neural networks considered are Long Short-Term Memory (LSTM), a Multi-Layer Perceptron (MLP), and an LSTM Gated Recurrent Unit (GRU). Among the metrics used to check the algorithm's performance, the Mean Absolute Percentage Error (MAPE) indicates that the values of this research are coherent with other scenarios to forecast solar energy; the boundary conditions were not the same, however. The lowest MAPE was observed in the city of Labrea with the LSTM GRU.

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confidence: 99%