Deep Learning for Energy Time-Series Analysis and Forecasting
It provides a practical guide for researchers and practitioners in the energy sector, but is incremental as it synthesizes existing methods without introducing new innovations.
This paper reviews deep learning methods for energy time-series forecasting, focusing on tasks like electric load demand and renewable energy generation, with an emphasis on applying these techniques in the Greek Energy Market.
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.