Relação entre demanda de energia elétrica e temperatura para concessionárias do Rio Grande do Sul

• Ano de defesa: 2019
• Autor(a) principal: Wilke, Ana Luiza Dors
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Brasil; Meteorologia; UFSM; Programa de Pós-Graduação em Meteorologia; Centro de Ciências Naturais e Exatas
• Palavras-chave em Português: Demanda de energia elétrica; Temperatura do ar; Energia elétrica e fatores meteorológicos; Modelo weather research and forecasting (WRF); Decomposição em modos empíricos (DME); Power electrical demand; Air temperature; Electrical energy and meteorological features; Weather research and forecasting model (WRF); EMD; CNPQ::CIENCIAS EXATAS E DA TERRA::GEOCIENCIAS::METEOROLOGIA
• Link de acesso: http://repositorio.ufsm.br/handle/1/19711

This thesis aims to describe the correlation between the different characteristic air temperatures in the state of Rio Grande do Sul and the demand for electrical energy (DEE) registered for three regions (A, B and C)2 in the same state in southern Brazil in 2014. The power consumption data with time resolution of one hour was provided by National System Operator (ONS), that manages the electricity service in Brazil. This data was shared on the basis of Research and Development Project in a partnership between the Federal University of Santa Maria (UFSM) and Brazilian oil company Petrobras. Considering the lack of observed meteorological data, the temperature data was obtained from Weather Research and Forecasting (WRF) simulations. The characteristic temperatures were defined by the weighted mean of the simulated temperature data regarding the area covered by the regions and the number of consumers. The correlation coefficients between the two samples were determined by Pearson and Spearman correlations. The samples were divided in two groups: weekdays, and weekends and holidays. Besides the correlations, the samples were decomposed by Complete Ensemble Empirical Mode Decomposition (CEEMD). The correlation coefficients show a strong relation between DEE and temperature, mainly for the weekdays sample. Furthermore, these values exhibit a variation during the day with less correlated data in the morning and evening transitions for regions A and B. The CEEMD show that daily scale and residual decomposition are the most significant modes in a year period for these two power providers too. The correlation coefficients for region C is generally above to 0.5. This feature denotes a less significant temperature rule over DEE in that region. The C cover area is characterized as a more developed industrial region than A and B. In such manner, the temperature seems to affect domestic electrical users more intensely than industrial ones. This behavior of region C is observed on decomposed scales as well. In this case, the residual modes of temperature and DEE are completely uncorrelated, as well as, the oscillating scales. For the other regions the most relevant scales are well correlated. It shows that when the temperature is an important parameter on DEE the dominant scales in terms of variability are correlated.