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dc.contributor.authorPérez de Rojas, Ignacioes-ES
dc.contributor.otherUniversidad Pontificia Comillas,es_ES
dc.date.accessioned2018-02-27T16:38:00Z
dc.date.availablees_ES
dc.date.issued2018es_ES
dc.identifier.urihttp://hdl.handle.net/11531/26076
dc.descriptionThis project aims to predict the setpoints that an occupant would choose for their house based on current external variables such as outside temperature, humidity, occupancy, etc. This will be done using regression and clustering techniques, tailoring the predictions of each occupant given their preferences and how the other occupants in their cluster behave, which will prove useful when modeling the total energy consumption of a building or a group of buildings.es_ES
dc.description.abstractLos sistemas de climatización representan aproximadamente el 21% del consumo total de energía en el sector residencial en EEUU. Pese a que su impacto a veces se pasa por alto, los termostatos residenciales tienen un gran efecto en el consumo total de energía de los hogares de EEUU. Este documento analiza los datos de más de 6500 termostatos inteligentes para comprender cómo los ocupantes se comportan e interactúan con sus termostatos, y tiene tres objetivos principales: (1) proporcionar un análisis exploratorio de los datos para visualizar los diversos patrones de uso del termostato; (2) analizar las correlaciones lineales y no lineales entre los setpoints de temperatura en hogares, con variables de entrada como la temperatura exterior, la humedad o la ubicación geográfica; (3) clasificar a los usuarios de acuerdo con sus preferencias personales y localización para resaltar las diferencias entre los hogares acostumbrados a diferentes condiciones climáticas.es-ES
dc.description.abstractHVAC systems account for about 21% of the total energy consumption in the residential sector in the U.S. Although their impact is sometimes overlooked, residential thermostats have a great effect on the overall energy consumption of U.S. households. This document analyzes the data from more than 6500 smart thermostats to better understand how occupants behave and interact with their thermostats, and has a three-folded objective: (1) providing an exploratory analysis of the data to visualize the various patterns of thermostat usage; (2) analyzing the linear and non-linear correlations among the temperature setpoints in many households with other variables such as outdoor temperature, relative humidity, or the geographical location; (3) classifying users according to their personal preferences and geographical location to highlight any differences between households accustomed to different weather conditions.en-GB
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoen-GBes_ES
dc.subject.otherMII-O (H62-organizacion)es_ES
dc.titleThe impact of climate, geographical location, and human behavior on usage patterns of programmable thermostatses_ES
dc.typeinfo:eu-repo/semantics/masterThesises_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.keywordstermostato, inteligente, prediccion, setpointes-ES
dc.keywordsthermostat, smart, prediction, setpointen-GB


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