Metodologia de inteligência de negócios para recomendação de alocação de fontes renováveis de energia
Carregando...
Data
2022-08-08
Autores
Palma, Eloá Carolina Nava Cardoso
Título da Revista
ISSN da Revista
Título de Volume
Editor
Universidade Estadual Paulista (Unesp)
Resumo
O presente trabalho propõe uma metodologia de Inteligência de Negócios (BI - Business Intelligence) a fim de auxiliar no processo de tomada de decisão e recomendação para a implantação de energias renováveis (eólica e solar) em determinada localização do território brasileiro. Essa metodologia é sistematizada num modelo hierárquico de quatro camadas, que considera a influência de múltiplas variáveis (técnica, econômica, geológica, climatológica e ambiental) para aprimorar a indicação de regiões favoráveis para a implantação de energia renovável. A primeira camada é a de Mineração de Dados, responsável pela captação de dados brutos dessa variáveis e transformação em informação. Esses dados brutos têm origem em diferentes fontes de dados oficiais (Global Solar Atlas, Global Wind Atlas, IBGE, Cadastro Nacional de Unidades de Conservação, Operador Nacional do Sistema e EMBRAPA), sendo transformados em mapas das variáveis com pontos georreferenciados (grid de 10 km x 10 km) em todo o território brasileiro. A segunda camada é a de Análise Fuzzy, baseada em um sistema de inferência com regras estabelecidas a fim de gerar a variável Potencial Eólico. A terceira camada é a de Análise AHP (Analytic Hierarchy Process), responsável pela análise multicritério das variáveis Potencial Solar, Potencial Eólico, Investimento Solar, Investimento Eólico, Declividade do solo, Geotecnia, Distância física das Linhas de Transmissão de energia elétrica e Consumo de Energia Elétrica. Essa camada reclassifica os valores dessas variáveis, através de uma hierarquia de pesos, padronizando suas saídas em índices Muito Baixo, Baixo, Moderado, Alto e Muito Alto. A quarta camada é a da Indicação, responsável pela álgebra de mapas das variáveis da análise AHP em conjunto com as áreas de unidades de conservação ambiental nacional. O resultado dessa camada é um mapa georreferenciado destacando as regiões do território brasileiro de acordo com seu potencial de implantação de energia solar e/ou eólica. A análise geral e em regiões específicas desses mapas, em confronto com suas indicações, comprovam a coerência dos resultados e validam a metodologia desenvolvida. Finalmente, a recomendação de implantação de energia solar e/ou eólica pode ser realizada com base no índice de favorabilidade ou indicação da fonte naquela localidade no território brasileiro
The present work proposes a methodology of Business Intelligence (BI- Business Intelligence) in order to assist in the decision-making and recommendation process for the implementation of renewable energies (wind and solar) in a certain location of the Brazilian territory. This methodology is systematized in a four-layer hierarchical model, which considers the influence of multiple variables (technical, economic, geological, climatological and environmental) to improve the indication of favorable regions for the implementation of renewable energy. The first layer is Data Mining, responsible for capturing raw data from these variables and transforming them into information. These raw data originate from different official data sources (Global Solar Atlas, Global Wind Atlas, IBGE, National Registry of Conservation Units, National System Operator and EMBRAPA), being transformed into maps of the variables with georeferenced points (grid of 10 km x 10 km) throughout the Brazilian territory. The second layer is Fuzzy Analysis, based on an inference system with established rules in order to generate the Wind Potential variable. The third layer is the AHP Analysis (Analytic Hierarchy Process), responsible for the multicriteria analysis of the variables Solar Potential, Wind Potential, Solar Investment, Wind Investment, Soil Slope, Geotechnics, Physical Distance from Power Transmission Lines Electricity and Electricity Consumption. This layer reclassifies the values of these variables, through a hierarchy of weights, standardizing their outputs in Very Low, Low, Moderate, High and Very High indices. The fourth layer is the Indication layer, responsible for the algebra of maps of the variables of the AHP analysis together with the areas of national environmental conservation units. The result of this layer is a georeferenced map highlighting the regions of the Brazilian territory according to their potential for deploying solar and/or wind energy. The general analysis and in specific regions of these maps, in comparison with their indications, prove the coherence of the results and validate the developed methodology. Finally, the recommendation to implement solar and/or wind energy can be made based on the favorability index or indication of the source in that location in Brazilian territory.
The present work proposes a methodology of Business Intelligence (BI- Business Intelligence) in order to assist in the decision-making and recommendation process for the implementation of renewable energies (wind and solar) in a certain location of the Brazilian territory. This methodology is systematized in a four-layer hierarchical model, which considers the influence of multiple variables (technical, economic, geological, climatological and environmental) to improve the indication of favorable regions for the implementation of renewable energy. The first layer is Data Mining, responsible for capturing raw data from these variables and transforming them into information. These raw data originate from different official data sources (Global Solar Atlas, Global Wind Atlas, IBGE, National Registry of Conservation Units, National System Operator and EMBRAPA), being transformed into maps of the variables with georeferenced points (grid of 10 km x 10 km) throughout the Brazilian territory. The second layer is Fuzzy Analysis, based on an inference system with established rules in order to generate the Wind Potential variable. The third layer is the AHP Analysis (Analytic Hierarchy Process), responsible for the multicriteria analysis of the variables Solar Potential, Wind Potential, Solar Investment, Wind Investment, Soil Slope, Geotechnics, Physical Distance from Power Transmission Lines Electricity and Electricity Consumption. This layer reclassifies the values of these variables, through a hierarchy of weights, standardizing their outputs in Very Low, Low, Moderate, High and Very High indices. The fourth layer is the Indication layer, responsible for the algebra of maps of the variables of the AHP analysis together with the areas of national environmental conservation units. The result of this layer is a georeferenced map highlighting the regions of the Brazilian territory according to their potential for deploying solar and/or wind energy. The general analysis and in specific regions of these maps, in comparison with their indications, prove the coherence of the results and validate the developed methodology. Finally, the recommendation to implement solar and/or wind energy can be made based on the favorability index or indication of the source in that location in Brazilian territory.
Descrição
Palavras-chave
Energias renováveis, Lógica fuzzy, AHP, Sistemas de apoio a decisão, Business Intelligence, Renewable energies, Fuzzy logic, Decision support systems, Business intelligence, Wind energy, Solar energy