000109340 001__ 109340
000109340 005__ 20230519145421.0
000109340 0247_ $$2doi$$a10.1109/ACCESS.2021.3129930
000109340 0248_ $$2sideral$$a125396
000109340 037__ $$aART-2021-125396
000109340 041__ $$aeng
000109340 100__ $$aGuillen Asensio, A.
000109340 245__ $$aEnergy shortage failure prediction in photovoltaic standalone installations by using machine learning techniques
000109340 260__ $$c2021
000109340 5060_ $$aAccess copy available to the general public$$fUnrestricted
000109340 5203_ $$aThe use of energy storage systems in standalone photovoltaic installations is essential to supply energy demands, independently of solar generation. Accurate prediction of the battery state is critical for the safe, durable, and reliable operation of systems in this type of installations. In this study, an installation located in the area of Aragon (Spain) has been considered. Two methods, based on different types of Recurrent Neural Networks (RNN), are proposed to predict the battery voltage of the installation two days ahead. Specifically, the Nonlinear Auto Regressive with Exogenous Input (NARX) network and the Long Short-Term Memory (LSTM) network are studied and compared. The implemented algorithms process battery voltage, temperature and current waveforms; and rely on the selection of different future scenarios based on weather forecasting to estimate the future voltage of the battery. The proposed methodology is capable of predicting the voltage with a Root Mean Squared Error (RMSE) error of 1.2 V for batteries of 48 V, in critical situations where the installation is running out of energy. The study contributes to the ongoing research of developing preventive control systems that help reduce costs and improve the performance of remote energy storage systems based on renewable energies with a positive outcome.
000109340 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000109340 590__ $$a3.476$$b2021
000109340 592__ $$a0.927$$b2021
000109340 594__ $$a6.7$$b2021
000109340 591__ $$aCOMPUTER SCIENCE, INFORMATION SYSTEMS$$b79 / 164 = 0.482$$c2021$$dQ2$$eT2
000109340 591__ $$aTELECOMMUNICATIONS$$b43 / 93 = 0.462$$c2021$$dQ2$$eT2
000109340 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b105 / 277 = 0.379$$c2021$$dQ2$$eT2
000109340 593__ $$aComputer Science (miscellaneous)$$c2021$$dQ1
000109340 593__ $$aEngineering (miscellaneous)$$c2021$$dQ1
000109340 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000109340 700__ $$aSanz Gorrachategui, I.
000109340 700__ $$aBono Nuez, A.
000109340 700__ $$aBernal, C.
000109340 700__ $$aSanz Alcaine, J. M.
000109340 700__ $$aPérez Cebolla, F. J.
000109340 773__ $$g9 (2021), 128660-128671$$pIEEE Access$$tIEEE Access$$x2169-3536
000109340 8564_ $$s1746998$$uhttps://zaguan.unizar.es/record/109340/files/texto_completo.pdf$$yVersión publicada
000109340 8564_ $$s2658717$$uhttps://zaguan.unizar.es/record/109340/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000109340 909CO $$ooai:zaguan.unizar.es:109340$$particulos$$pdriver
000109340 951__ $$a2023-05-18-14:07:06
000109340 980__ $$aARTICLE