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驱动因素来预测未来的过程。在能源时间序列分析与预测的广泛领域中，涵盖多项具体任务，其中电力负荷需求预测、个性化能耗预测以及可再生能源发电预测是最常见的研究方向。鉴于深度学习（DL）在视觉任务领域的卓越表现，DL模型已被成功应用于时间序列预测任务。本文旨在深入探讨多种面向提升能源时间序列预测性能的DL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的必要知识储备。