Diagnóstico de la demanda del consumo de energía eléctrica en un Smart Home, enfocado en el sector residencial de Quito, durante el año 2015, barrio La Kennedy. Caracterización y optimización del consumo de energía eléctrica.
The main goal of this project is to present a possible solution to a problem that is been created by the advance of technology and the implementation of Smart cities “Smart Grid” through studying the characterization and modeling the daily electricity demand curve. This curve considers the growth of...
محفوظ في:
| المؤلف الرئيسي: | |
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| التنسيق: | masterThesis |
| اللغة: | spa |
| منشور في: |
2015
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://repositorio.utc.edu.ec/handle/27000/6163 |
| الوسوم: |
إضافة وسم
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إضافة وسم | الملخص: | The main goal of this project is to present a possible solution to a problem that is been created by the advance of technology and the implementation of Smart cities “Smart Grid” through studying the characterization and modeling the daily electricity demand curve. This curve considers the growth of technology and therefore the increase of consumption of electricity of residential users, generating a significant impact on the demand curve and the users’ economy. For this reason, the suggested optimization will allow a balance between comfort and energy consumption of users. The research is divided into four chapters: the first one presents the state of art for demand modeling and optimization, the second one develops the research methodology and determines the sample where the surveys will take place. The third chapter deals with tabulation of information obtained in surveys conducted in Kennedy neighborhood in Quito. Also, it includes an analysis of the measurement data by user type energy analyzer. Chapter IV, that is the last one, develops the proposal by modeling the demand by Markov Chains and Monte Carlo (MCMC). This modeling established different scenarios, which characterize the energy consumption and the optimization that was performed by means of the Pareto multi objective method. The problem was solved through experimenting a simulation and modeling a demand to optimize energy, generating a 20% of savings in electricity consumption, generating a benefit to the environment and reducing CO2 emissions; without changing the habits of users. |
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