%0 Journal Article %A Collado Villaverde, Armando %A Muñoz Martínez, Pablo %A Cid Tortuero, Consuelo %T Deep Neural Networks with Convolutional and LSTM layers for SYM-H and ASY-H forecasting %D 2021 %@ 1542-7390 %U http://hdl.handle.net/10017/48669 %X 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. %K Energy management %K Heating %K Ventilation and air conditioning systems %K Autonomic computing, Machine learning %K Multi-objective optimization %K Smart building %K Informática %K Computer science %K Astronomía %K Astronomy %~ Biblioteca Universidad de Alcala