Mostrar el registro sencillo del ítem
Time Series Forecasting for Energy Consumption
dc.contributor.author | Pegalajar Jiménez, María Del Carmen | |
dc.contributor.author | Baca Ruiz, Luis Gonzaga | |
dc.date.accessioned | 2024-02-07T20:14:37Z | |
dc.date.available | 2024-02-07T20:14:37Z | |
dc.date.issued | 2022-01-21 | |
dc.identifier.citation | Pegalajar, M.C.; Ruiz, L.G.B. Time Series Forecasting for Energy Consumption. Energies 2022, 15, 773. https://doi.org/10.3390/en15030773 | es_ES |
dc.identifier.uri | https://hdl.handle.net/10481/88633 | |
dc.description.abstract | In the last few years, there has been considerable progress in time series forecasting algorithms, which are becoming more and more accurate, and their applications are numerous and varied. Specifically, accurately predicting energy consumption in a particular building, country, etc., is an important task for properly managing energy efficiency. Moreover, it can be advantageous to carry this out in a short time frame, taking into account the new consumption paradigm. On the other hand, the time horizon must be considered, which can be short-, medium-, or long-term. For this reason, it is important to develop and implement new intelligent models faster and more accurately. In this way, the application of big data and machine learning techniques have become essential to achieve this goal. This Special Issue sought to contribute to the advancement of energy consumption prediction using artificial intelligence models in an optimal and precise manner. | es_ES |
dc.description.sponsorship | PID2020-112495RB-C21 | es_ES |
dc.description.sponsorship | B-TIC-42-UGR20 | es_ES |
dc.description.sponsorship | “Next Generation EU” Margaritas Salas | es_ES |
dc.language.iso | eng | es_ES |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Artificial intelligence | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | Renewable energy, solar power, and wind power | es_ES |
dc.subject | Deep learning | es_ES |
dc.subject | Artificial neural networks | es_ES |
dc.subject | Data mining | es_ES |
dc.title | Time Series Forecasting for Energy Consumption | es_ES |
dc.type | other | es_ES |
dc.rights.accessRights | open access | es_ES |
dc.identifier.doi | 10.3390/en15030773 | |
dc.type.hasVersion | VoR | es_ES |