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.

翻译：能源时间序列分析描述了通过分析历史能源观测数据及可能的外部因素以预测未来的过程。能源时间序列分析与预测这一广泛领域涉及多项任务，其中最常见的包括电力负荷需求预测、个性化能源消耗预测以及可再生能源发电预测。得益于深度学习在视觉任务领域的卓越表现，深度学习模型已成功应用于时间序列预测任务。本文旨在深入探讨各类旨在提升能源时间序列预测任务性能的深度学习方法，特别聚焦于希腊能源市场，并为读者提供在实践中应用这些方法所必需的知识储备。