RT Journal Article
T1 Feasibility of different weather data sources applied to building indoor temperature estimation using LSTM neural networks
A1 Pensado Mariño, Martín
A1 Febrero Garrido, Lara
A1 Eguía Oller, Pablo
A1 Granada Álvarez, Enrique
K1 3305.90 Transmisión de Calor en la Edificación
K1 3328.16 Transferencia de Calor
K1 3322.04 Transmisión de Energía
AB The use of Machine Learning models is becoming increasingly widespread to assess energy performance of a building. In these models, the accuracy of the results depends largely on outdoor conditions. However, getting these data on-site is not always feasible. This article compares the temperature results obtained for an LSTM neural network model, using four types of meteorological data sources. The first is the monitoring carried out in the building; the second is a meteorological station near the site of the building; the third is a table of meteorological data obtained through a kriging process and the fourth is a dataset obtained using GFS. The results are analyzed using the CV(RSME) and NMBE indices. Based on these indices, in the four series, a CV(RSME) slightly higher than 3% is obtained, while the NMBE is below 1%, so it can be deduced that the sources used are interchangeable.
PB Sustainability
SN 20711050
YR 2021
FD 2021-12-13
LK http://hdl.handle.net/11093/2858
UL http://hdl.handle.net/11093/2858
LA eng
NO Sustainability, 13(24): 13735 (2021)
NO Ministerio de Ciencia, Innovación y Universidades | Ref. RTI2018-096296-B-C2
DS Investigo
RD 04-dic-2024