Deep Learning for Energy Time-Series Analysis and Forecasting
- URL: http://arxiv.org/abs/2306.09129v2
- Date: Thu, 29 Jun 2023 08:37:46 GMT
- Title: Deep Learning for Energy Time-Series Analysis and Forecasting
- Authors: Maria Tzelepi, Charalampos Symeonidis, Paraskevi Nousi, Efstratios
Kakaletsis, Theodoros Manousis, Pavlos Tosidis, Nikos Nikolaidis and
Anastasios Tefas
- Abstract summary: Energy time-series analysis describes the process of analyzing past energy observations and possibly external factors so as to predict the future.
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.
- Score: 42.617834983479796
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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