Diagnóstico energético para obtención de las curvas de demanda de los bloques A y B del Campus Matriz de la Universidad Técnica de Cotopaxi.
The identification of changes in consumption and the treatment of electricity demand data is an essential factor for planning renewable energy projects. University buildings today play an important role in the demand for electricity. For this reason, this work proposes a statistical method for the c...
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| Médium: | bachelorThesis |
| Jazyk: | spa |
| Vydáno: |
2021
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| On-line přístup: | http://repositorio.utc.edu.ec/handle/27000/8164 |
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Přidat tag | Shrnutí: | The identification of changes in consumption and the treatment of electricity demand data is an essential factor for planning renewable energy projects. University buildings today play an important role in the demand for electricity. For this reason, this work proposes a statistical method for the characterization of the monthly and annual electricity demand curves, based on historical data collected in a period of 5 years. The energy consumption data is the base for solving energy services, however, the raw data may not be applied directly in an optimization process; the reason is that they contain atypical data and distort the effectiveness of the method, this can lead to an inaccurate characterization of the electricity demand. The proposed method has centered on determining the patterns of electricity demand, the detection of typical and atypical data, the analysis of the variability of both monthly and annual demand, in order to obtain the electricity demand curve represented with the data from the maximum, average and minimum demand for a typical year. To distinguish these typical and atypical data from electricity demand, the normal distribution method was applied, which has produced a set of normalized data. The statistical method has been validated by non-parametric tests, including the Kolmogorov Smirnov test that was used for the raw and transformed data, in the end the test gave a satisfactory normality result. Therefore, the method demonstrated that it characterizes the electricity demand data with high confiability and it can also be used to process data provided by the smart meters that are currently being implemented in the institution. |
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