Time series forecasting techniques applied to hydroelectric generation systems

• Time series forecasting techniques applied to hydroelectric generation systems (HGS) are deeply examined. • Relevant hybrid methods applied to HGS during the last years are reviewed. • Artificial neural networks and hybrid models were the most widely used and best-performing techniques. • Identification of research gaps in HGS emphasizing the need for focused research in this area is discussed. Modeling sequential data over time has become an intensive and fast-growing research area. Time series analysis has many applications in the energy field. Time series modeling and forecasting applied to hydropower plants have become essential since reliable and accurate energy production forecasts are needed for capacity planning, scheduling, and power system operation. Although there are numerous recent works in the field of time series forecasting for hydroelectric production, as evidenced in the development of this studio, there are no systematic reviews on this topic. Most of the reviews found in the literature are broader and include optimization and control approaches in hydroelectric systems. In addition, the literature lacks research works revising and analyzing the application of time series forecasting techniques –from statistics, machine and deep learning, and soft computing– to hydropower production –in contrast to the other topics that have been more thoroughly studied, such as energy demand in buildings. This study shows an exhaustive review of the existing time series forecasting techniques applied to hydroelectric generation systems. A thorough literature search was conducted to outline and analyze the essential aspects of the time series forecasting models in hydropower systems. Statistical methods, regression and machine learning, deep learning, and other soft computing and hybrid models were examined. Other aspects, such as the country of origin of the hydropower project, timespan and resolution datasets, regressive and objective variables, and performance evaluation, were also studied and discussed. The results showed research gaps in literature. The findings of this research will help the energy research community improve energy production forecasting in hydropower plants.