RT Journal Article
T1 A solar altitude angle model for efficient solar energy predictions
A1 Herrería Alonso, Sergio
A1 Suárez González, Andrés
A1 Rodríguez Pérez, Miguel
A1 Rodríguez Rubio, Raúl Fernando
A1 López García, Candido Antonio
K1 2106.01 Energía Solar
K1 3322.02 Generación de Energía
K1 3322.05 Fuentes no Convencionales de Energía
AB Sunlight is one of the most frequently used ambient energy sources for energy harvesting in wireless sensor networks. Although virtually unlimited, solar radiation experiences significant variations depending on the weather, the season, and the time of day, so solar-powered nodes commonly employ solar prediction models to effectively adapt their energy demands to harvesting dynamics. We present in this paper a novel energy prediction model that makes use of the altitude angle of the sun at different times of day to predict future solar energy availability. Unlike most of the state-of-the-art predictors that use past energy observations to make predictions, our model does not require one to maintain local energy harvesting patterns of past days. Performance evaluation shows that our scheme is able to provide accurate predictions for arbitrary forecasting horizons by performing just a few low complexity operations. Moreover, our proposal is extremely simple to set up since it does not require any particular tuning for each different scenario or location.
PB Sensors
SN 14248220
YR 2020
FD 2020-03-04
LK http://hdl.handle.net/11093/1759
UL http://hdl.handle.net/11093/1759
LA eng
NO Sensors, 20(5): 1391 (2020)
NO Atlantic Research Center for Information and Communication Technologies | Ref. 0
DS Investigo
RD 17-mar-2025