RT info:eu-repo/semantics/article
T1 Deep Neural Networks with Convolutional and LSTM layers for SYM-H and ASY-H forecasting
A1 Collado Villaverde, Armando
A1 Muñoz Martínez, Pablo
A1 Cid Tortuero, Consuelo
K1 Energy management
K1 Heating
K1 Ventilation and air conditioning systems
K1 Autonomic computing, Machine learning
K1 Multi-objective optimization
K1 Smart building
K1 Informática
K1 Computer science
K1 Astronomía
K1 Astronomy
AB Geomagnetic indices quantify the disturbance caused by the solar activity on a planetary scale or in particular regions of the Earth. Among them, the SYM-H and ASY-H indices represent the (longitudinally) symmetric and asymmetric geomagnetic disturbance of the horizontal component of the magnetic field at midlatitude with a 1-min resolution. Their resolution, along with their relation to the solar wind parameters, makes the forecasting of the geomagnetic indices a problem that can be addressed through the use of Deep Learning, particularly using Deep Neural Networks (DNNs). In this work, we present two DNNs developed to forecast respectively the SYM-H and ASY-H indices. Both networks have been trained using the Interplanetary Magnetic Field (IMF) and the related index for the solar storms occurred in the last two solar cycles. As a result, the networks are able to accurately forecast the indices 2 h in advance, considering the IMF and indices values for the previous 200 min. The evaluation of both networks reveals a great forecasting precision, including good predictions for large storms that occurred during the solar cycle 23 and comparing with the persistence model for the period 2013-2020.
PB AGU
SN 1542-7390
YR 2021
FD 2021-04-30
LK http://hdl.handle.net/10017/48669
UL http://hdl.handle.net/10017/48669
LA eng
NO Junta de Comunidades de Castilla-La Mancha
DS MINDS@UW
RD 25-abr-2024