• Energy Research

Resumo A instalação de sistemas fotovoltaicos mostra-se uma alternativa promissora para a geração sustentável de energia elétrica. A realização de estudos de viabilidade de instalações fotovoltaicas é de extrema importância para se verificar a disponibilidade do recurso solar no local de instalação. Esta pesquisa tem como objetivo comparar os resultados da análise econômica de um sistema fotovoltaico com diferentes estimativas de geração de energia elétrica, utilizando diferentes bases de dados de irradiação solar. Tendo como objeto de estudo o sistema fotovoltaico do campus Floriano do IFPI, foram obtidos dados de irradiação solar a partir de diferentes bancos e softwares dedicados, e estimada a produção de energia elétrica. Esta foi comparada com os dados reais de geração, obtidos através do software de monitoramento dos inversores do sistema, e submetida a testes estatísticos. Para cada amostra foi realizado um estudo econômico, utilizando os conceitos da engenharia econômica. Os resultados mostram que a escolha da metodologia de cálculo das estimativas de geração pode influenciar diretamente nos resultados das análises de viabilidade, o que pode levar a escolhas erradas. Dependendo do tamanho do erro, problemas podem surgir durante a utilização do sistema a curto, médio e longo prazo.

This paper presents an experience applied to a public university campus using Data Envelopment Analysis (DEA) to evaluate and improve the energy efficiency of the indoor spaces of the buildings within the limits of the Federal University of Piauí, considering the lighting (installed power and luminous flux) and air conditioning (absorbed electric power and cooling capacity) input/output variables. Using Brazilian energy efficiency evaluation methods, a comparison was made between DEA and Brazilian standards, with the goal of examining DEA’s performance and feasibility in efficiency improvement. The results revealed that all of the analyzed university classrooms were inefficient, which is coherent with the classification obtained by applying Brazilian standards; the calculated efficiency scores for these rooms varied from 0.7182 to 0.8477, a 0.7848 average. The DEA model, while operating in lighting and air conditioning systems, achieved a reduction of installed power of 43.5% and 22.7%, respectively, totaling a decrease of 25.6%, being able to maintain the standard characteristics of the systems mentioned. According to the DEA models, it was found that the generated targets effectively improved the efficiency of lighting and air-conditioning systems, reducing excessive inputs such as air conditioners’ consumption and increasing deficient outputs such as luminous flux. It is possible to expand this successful application in the layout of the building in the whole campus area to concept small smart city projects; both have been achieved in the public buildings of the administrative body. Results from this paper revealed DEA’s potential in assessing and optimizing the energy efficiency of buildings, improving their sustainability indexes, acting as a tool to support decision-making and benchmarking.

The diagnosis of incipient faults in power system elements such as transformers is usually based on the concentrations of dissolved gases existent in the insulation oil. There are consolidated DGA-based (dissolved gas analysis) methods in the literature, such as the Duval triangle. However, they present some limitations such as the existence of non-decision areas and erroneous results. Proposed is a simple methodology to improve the analysis of incipient faults based on rules extracted from a high-dimension space (21 attributes), formed by the gases concentrations and some of their interrelations. From such input space, the C4.5 method (decision tree) is used to extract a set of interpretable rules. Databases known in the DGA technical literature such as IEC TC 10 are adopted to analyse the proposed approach. When compared with a standard method, considering all data test folders in the performed 10-folder cross-validation statistical analysis, the extracted rules show greater accuracy with an error in the diagnosis of incipient faults of 6.25%, against 18.75% for the Triangle method in the worst case.