Neural Networks Forecast Models Comparison for the Solar Energy Generation in Amazon Basin
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 coun...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10414097/ |
| _version_ | 1797289860696899584 |
|---|---|
| author | Andre Luis Ferreira Marques Marcio Jose Teixeira Felipe Valencia De Almeida Pedro Luiz Pizzigatti Correa |
| author_facet | Andre Luis Ferreira Marques Marcio Jose Teixeira Felipe Valencia De Almeida Pedro Luiz Pizzigatti Correa |
| author_sort | Andre Luis Ferreira Marques |
| collection | DOAJ |
| description | 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/<inline-formula> <tex-math notation="LaTeX">$\text{m}^{2}$ </tex-math></inline-formula>) 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. |
| first_indexed | 2024-03-07T19:12:07Z |
| format | Article |
| id | doaj.art-41f27f4737594efe87b6953afbaa510b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-07T19:12:07Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-41f27f4737594efe87b6953afbaa510b2024-03-01T00:00:20ZengIEEEIEEE Access2169-35362024-01-0112179151792510.1109/ACCESS.2024.335833910414097Neural Networks Forecast Models Comparison for the Solar Energy Generation in Amazon BasinAndre Luis Ferreira Marques0https://orcid.org/0000-0003-4110-9398Marcio Jose Teixeira1https://orcid.org/0000-0002-9164-675XFelipe Valencia De Almeida2https://orcid.org/0000-0003-2031-6443Pedro Luiz Pizzigatti Correa3Polytechnic School, University of São Paulo, São Paulo, BrazilInstitute of Physics, University of São Paulo, Sao Pãulo, BrazilPolytechnic School, University of São Paulo, São Paulo, BrazilPolytechnic School, University of São Paulo, São Paulo, BrazilDeep 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/<inline-formula> <tex-math notation="LaTeX">$\text{m}^{2}$ </tex-math></inline-formula>) 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.https://ieeexplore.ieee.org/document/10414097/Deep learninglong short-term memorymulti-layer perceptrondata scienceAmazon basinsolar energy |
| spellingShingle | Andre Luis Ferreira Marques Marcio Jose Teixeira Felipe Valencia De Almeida Pedro Luiz Pizzigatti Correa Neural Networks Forecast Models Comparison for the Solar Energy Generation in Amazon Basin IEEE Access Deep learning long short-term memory multi-layer perceptron data science Amazon basin solar energy |
| title | Neural Networks Forecast Models Comparison for the Solar Energy Generation in Amazon Basin |
| title_full | Neural Networks Forecast Models Comparison for the Solar Energy Generation in Amazon Basin |
| title_fullStr | Neural Networks Forecast Models Comparison for the Solar Energy Generation in Amazon Basin |
| title_full_unstemmed | Neural Networks Forecast Models Comparison for the Solar Energy Generation in Amazon Basin |
| title_short | Neural Networks Forecast Models Comparison for the Solar Energy Generation in Amazon Basin |
| title_sort | neural networks forecast models comparison for the solar energy generation in amazon basin |
| topic | Deep learning long short-term memory multi-layer perceptron data science Amazon basin solar energy |
| url | https://ieeexplore.ieee.org/document/10414097/ |
| work_keys_str_mv | AT andreluisferreiramarques neuralnetworksforecastmodelscomparisonforthesolarenergygenerationinamazonbasin AT marciojoseteixeira neuralnetworksforecastmodelscomparisonforthesolarenergygenerationinamazonbasin AT felipevalenciadealmeida neuralnetworksforecastmodelscomparisonforthesolarenergygenerationinamazonbasin AT pedroluizpizzigatticorrea neuralnetworksforecastmodelscomparisonforthesolarenergygenerationinamazonbasin |