Time series forecasting techniques applied to hydroelectric generation systems
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 a...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | International Journal of Electrical Power & Energy Systems |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061524006483 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832595377021255680 |
|---|---|
| author | Julio Barzola-Monteses Juan Gómez-Romero Mayken Espinoza-Andaluz Waldo Fajardo |
| author_facet | Julio Barzola-Monteses Juan Gómez-Romero Mayken Espinoza-Andaluz Waldo Fajardo |
| author_sort | Julio Barzola-Monteses |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-16edf81745094513b6593576ff7cbeca |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-16edf81745094513b6593576ff7cbeca2025-01-19T06:23:59ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-03-01164110424Time series forecasting techniques applied to hydroelectric generation systemsJulio Barzola-Monteses0Juan Gómez-Romero1Mayken Espinoza-Andaluz2Waldo Fajardo3Department of Computer Science and Artificial Intelligence, Escuela Técnica Superior de Ingenierías Informática y de Telecomunicación, Universidad de Granada, 1807, Granada, Spain; Artificial Intelligence research group, University of Guayaquil, 090514, Guayaquil, Ecuador; Corresponding author.Department of Computer Science and Artificial Intelligence, Escuela Técnica Superior de Ingenierías Informática y de Telecomunicación, Universidad de Granada, 1807, Granada, SpainCentro de Energías Renovables y Alternativas, Facultad de Ingeniería Mecánica y Ciencias de la Producción, Escuela Superior Politécnica del Litoral, 09-01-5863, Guayaquil, EcuadorDepartment of Computer Science and Artificial Intelligence, Escuela Técnica Superior de Ingenierías Informática y de Telecomunicación, Universidad de Granada, 1807, Granada, SpainModeling 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.http://www.sciencedirect.com/science/article/pii/S0142061524006483Deep learningForecastingHybrid modelsHydropowerMachine learningSoft computing |
| spellingShingle | Julio Barzola-Monteses Juan Gómez-Romero Mayken Espinoza-Andaluz Waldo Fajardo Time series forecasting techniques applied to hydroelectric generation systems International Journal of Electrical Power & Energy Systems Deep learning Forecasting Hybrid models Hydropower Machine learning Soft computing |
| title | Time series forecasting techniques applied to hydroelectric generation systems |
| title_full | Time series forecasting techniques applied to hydroelectric generation systems |
| title_fullStr | Time series forecasting techniques applied to hydroelectric generation systems |
| title_full_unstemmed | Time series forecasting techniques applied to hydroelectric generation systems |
| title_short | Time series forecasting techniques applied to hydroelectric generation systems |
| title_sort | time series forecasting techniques applied to hydroelectric generation systems |
| topic | Deep learning Forecasting Hybrid models Hydropower Machine learning Soft computing |
| url | http://www.sciencedirect.com/science/article/pii/S0142061524006483 |
| work_keys_str_mv | AT juliobarzolamonteses timeseriesforecastingtechniquesappliedtohydroelectricgenerationsystems AT juangomezromero timeseriesforecastingtechniquesappliedtohydroelectricgenerationsystems AT maykenespinozaandaluz timeseriesforecastingtechniquesappliedtohydroelectricgenerationsystems AT waldofajardo timeseriesforecastingtechniquesappliedtohydroelectricgenerationsystems |