Respuesta a la Demanda para Smart Home Utilizando Procesos Estocásticos

Translated title of the contribution: Demand Response for Smart Home Using Stochastic Processes

Pablo Alejandro Moreno Falcony, Edwin Marcelo Garcia Torres

Research output: Contribution to journalArticle


The increase of energy consumption in end users, especially in residential users, implies that the electrical system grows at the same pace, both in infrastructure and in installed power, besides energy prices vary to meet these needs, so this work uses the demand response methodology using stochastic methods such as Markov in order to optimize energy consumption in residential users. It is necessary the participation of the customers in the electric system, since in this way it is possible to verify the real amount of load that exists in the network at a certain time, and this helps the electric systems to be more reliable and efficient, giving guarantees at the time of giving an energy supply. In addition, optimizing energy consumption results in lower CO2 emissions to the environment by relying less on fossil fuel power plants, which implies a reduction in global pollution, an issue that is of paramount importance today. Although there are models for energy optimization, the reality is that the consumption of a house is much more complex, since it has variables such as geographical location, architecture, materials used for the design, arrangement of windows, number of occupants, climate, season of the year. So, when applying demand response in residential environments, it is important to take into account basic criteria, such as maintaining the comfort of the end user, since this way a sustained participation of the demand response is achieved, having individual participation would require a large investment in control and communication technology.
Translated title of the contributionDemand Response for Smart Home Using Stochastic Processes
Original languageSpanish (Ecuador)
Pages (from-to)7-17
Number of pages11
JournalRevista De I+D Tecnológico
Issue number12
StatePublished - 30 Dec 2016


  • Automation
  • Customers
  • Demand Response
  • Efficiency
  • Energy
  • Generation
  • Load
  • Residential
  • Service
  • Software

CACES Knowledge Areas

  • 317A Electricity and Energy


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