Deep Learning for Energy Time-Series Analysis and Forecasting

Autor:	Tzelepi, Maria, Symeonidis, Charalampos, Nousi, Paraskevi, Kakaletsis, Efstratios, Manousis, Theodoros, Tosidis, Pavlos, Nikolaidis, Nikos, Tefas, Anastasios
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Energy time-series analysis describes the process of analyzing past energy observations and possibly external factors so as to predict the future. Different tasks are involved in the general field of energy time-series analysis and forecasting, with electric load demand forecasting, personalized energy consumption forecasting, as well as renewable energy generation forecasting being among the most common ones. Following the exceptional performance of Deep Learning (DL) in a broad area of vision tasks, DL models have successfully been utilized in time-series forecasting tasks. This paper aims to provide insight into various DL methods geared towards improving the performance in energy time-series forecasting tasks, with special emphasis in Greek Energy Market, and equip the reader with the necessary knowledge to apply these methods in practice. Comment: 13 papges, 4 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.09129 Zobrazit plný text záznamu View this record from Arxiv