Time Series Forecasting for Energy Consumption
Metadatos
Mostrar el registro completo del ítemMateria
Artificial intelligence Machine learning Renewable energy, solar power, and wind power Deep learning Artificial neural networks Data mining
Fecha
2022-01-21Referencia bibliográfica
Pegalajar, M.C.; Ruiz, L.G.B. Time Series Forecasting for Energy Consumption. Energies 2022, 15, 773. https://doi.org/10.3390/en15030773
Patrocinador
PID2020-112495RB-C21; B-TIC-42-UGR20; “Next Generation EU” Margaritas SalasResumen
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.