RT info:eu-repo/semantics/bachelorThesis
T1 Energy load forecast in smart buildings with deep learning techniques
A1 Barón García, Alejandro
A2 Universidad de Valladolid. Escuela de Ingeniería Informática de Valladolid
K1 Time
K1 Series
K1 Energy
AB Predicting energy load is a growing problem these days. The need to study in advance howelectricity consumption will behave is key to resource management.Especially interesting is the case of the so-called Smart Buildings, buildings born from the trendtowards sustainable development and consumption which is increasingly in vogue, becomingmandatory by law in many countries.One type of model that constitutes an important part of the state of the art are the modelsbased on Deep Learning. These models represented great advances in Artificial Intelligencerecently, since although they were born in the 20th century, it has not been until 10 years agothat they have re-emerged thanks to the computational advances that allow them to be trainedby the general public.In this Final Degree Project, advanced Deep Learning techniques applied to the problem ofload prediction in Smart Buildings are presented, mainly basing the development on the datafrom the Alice Perry building of the National University of Ireland Galway, in collaborationwith the Informatics Research Unit for Sustainable Engineering of the same university.The datasets used were obtained from the time series of aggregated electricity consumptionof the air handling units (AHUs) in the Alice Perry building. Along with this information,historical weather data were also collected from the weather station in the same building inorder to study if these climatic variables help to a better prediction in the models.Time series prediction on this energy load data will be made in two different ways with hourlygranularity: one-step prediction in which studying the previous observations an estimate of thevalue of the load in the next hour is obtained and sequence prediction, in which we will try topredict the behaviour of the series in the next hours from the previous values.
YR 2020
FD 2020
LK http://uvadoc.uva.es/handle/10324/44114
UL http://uvadoc.uva.es/handle/10324/44114
LA eng
DS UVaDOC
RD 17-ago-2024