Articles | Volume 23, issue 6
https://doi.org/10.5194/hess-23-2735-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-23-2735-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Jun 2019
Research article |
| 28 Jun 2019
| 28 Jun 2019
Analysis of the effects of biases in ensemble streamflow prediction (ESP) forecasts on electricity production in hydropower reservoir management
Richard Arsenault
CORRESPONDING AUTHOR
Department of construction engineering, École de technologie
supérieure, Montréal, H3C 1K3, Canada
Quebec Power Operations, Rio Tinto, Jonquière, G7S 4R5, Canada
Pascal Côté
Quebec Power Operations, Rio Tinto, Jonquière, G7S 4R5, Canada
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Cited
19 citations as recorded by crossref.
- Comparative study of forecasting approaches in monthly streamflow series from Brazilian hydroelectric plants using Extreme Learning Machines and Box & Jenkins models J. Belotti et al. 10.2478/johh-2021-0001
- Impact of the quality of hydrological forecasts on the management and revenue of hydroelectric reservoirs – a conceptual approach M. Cassagnole et al. 10.5194/hess-25-1033-2021
- Quantifying streamflow predictive uncertainty for the optimization of short-term cascade hydropower stations operations Y. Liu et al. 10.1016/j.jhydrol.2021.127376
- Model Predictive Control of water resources systems: A review and research agenda A. Castelletti et al. 10.1016/j.arcontrol.2023.03.013
- Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants J. Belotti et al. 10.3390/en13184769
- Research on Reservoir Optimal Operation Based on Long-Term and Mid-Long-Term Nested Models C. Mo et al. 10.3390/w14040608
- Average domination: A new multi-objective value metric applied to assess the benefits of forecasts in reservoir operations under different flood design levels J. Quinn et al. 10.1016/j.advwatres.2024.104638
- Comparing a long short-term memory (LSTM) neural network with a physically-based hydrological model for streamflow forecasting over a Canadian catchment B. Sabzipour et al. 10.1016/j.jhydrol.2023.130380
- Coupling large-scale climate indices with a stochastic weather generator to improve long-term streamflow forecasts in a Canadian watershed S. Sohrabi et al. 10.1016/j.jhydrol.2020.125925
- Digitalization, Industry 4.0, Data, KPIs, Modelization and Forecast for Energy Production in Hydroelectric Power Plants: A Review C. Pepe & S. Zanoli 10.3390/en17040941
- Evaluation of Ensemble Inflow Forecasts for Reservoir Management in Flood Situations J. Mendes & R. Maia 10.3390/hydrology10020028
- Forecast Families: A New Method to Systematically Evaluate the Benefits of Improving the Skill of an Existing Forecast C. Rougé et al. 10.1061/JWRMD5.WRENG-5934
- Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods H. Siqueira et al. 10.3390/en13164236
- Effect of the quality of streamflow forecasts on the operation of cascade hydropower stations using stochastic optimization models Y. Liu et al. 10.1016/j.energy.2023.127298
- Evaluation of the potential of using subsets of historical climatological data for ensemble streamflow prediction (ESP) forecasting B. Sabzipour et al. 10.1016/j.jhydrol.2020.125656
- Long-Term Scheduling of Cascade Reservoirs Considering Inflow Forecasting Uncertainty Based on a Disaggregation Model X. Ding et al. 10.1007/s11269-020-02748-5
- Advancing Medium-Range Streamflow Forecasting for Large Hydropower Reservoirs in Brazil by Means of Continental-Scale Hydrological Modeling A. Kolling Neto et al. 10.3390/w15091693
- Sensitivity analysis of the hyperparameters of an ensemble Kalman filter application on a semi-distributed hydrological model for streamflow forecasting B. Sabzipour et al. 10.1016/j.jhydrol.2023.130251
- Multi-objective ensembles of echo state networks and extreme learning machines for streamflow series forecasting V. Ribeiro et al. 10.1016/j.engappai.2020.103910
19 citations as recorded by crossref.
- Comparative study of forecasting approaches in monthly streamflow series from Brazilian hydroelectric plants using Extreme Learning Machines and Box & Jenkins models J. Belotti et al. 10.2478/johh-2021-0001
- Impact of the quality of hydrological forecasts on the management and revenue of hydroelectric reservoirs – a conceptual approach M. Cassagnole et al. 10.5194/hess-25-1033-2021
- Quantifying streamflow predictive uncertainty for the optimization of short-term cascade hydropower stations operations Y. Liu et al. 10.1016/j.jhydrol.2021.127376
- Model Predictive Control of water resources systems: A review and research agenda A. Castelletti et al. 10.1016/j.arcontrol.2023.03.013
- Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants J. Belotti et al. 10.3390/en13184769
- Research on Reservoir Optimal Operation Based on Long-Term and Mid-Long-Term Nested Models C. Mo et al. 10.3390/w14040608
- Average domination: A new multi-objective value metric applied to assess the benefits of forecasts in reservoir operations under different flood design levels J. Quinn et al. 10.1016/j.advwatres.2024.104638
- Comparing a long short-term memory (LSTM) neural network with a physically-based hydrological model for streamflow forecasting over a Canadian catchment B. Sabzipour et al. 10.1016/j.jhydrol.2023.130380
- Coupling large-scale climate indices with a stochastic weather generator to improve long-term streamflow forecasts in a Canadian watershed S. Sohrabi et al. 10.1016/j.jhydrol.2020.125925
- Digitalization, Industry 4.0, Data, KPIs, Modelization and Forecast for Energy Production in Hydroelectric Power Plants: A Review C. Pepe & S. Zanoli 10.3390/en17040941
- Evaluation of Ensemble Inflow Forecasts for Reservoir Management in Flood Situations J. Mendes & R. Maia 10.3390/hydrology10020028
- Forecast Families: A New Method to Systematically Evaluate the Benefits of Improving the Skill of an Existing Forecast C. Rougé et al. 10.1061/JWRMD5.WRENG-5934
- Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods H. Siqueira et al. 10.3390/en13164236
- Effect of the quality of streamflow forecasts on the operation of cascade hydropower stations using stochastic optimization models Y. Liu et al. 10.1016/j.energy.2023.127298
- Evaluation of the potential of using subsets of historical climatological data for ensemble streamflow prediction (ESP) forecasting B. Sabzipour et al. 10.1016/j.jhydrol.2020.125656
- Long-Term Scheduling of Cascade Reservoirs Considering Inflow Forecasting Uncertainty Based on a Disaggregation Model X. Ding et al. 10.1007/s11269-020-02748-5
- Advancing Medium-Range Streamflow Forecasting for Large Hydropower Reservoirs in Brazil by Means of Continental-Scale Hydrological Modeling A. Kolling Neto et al. 10.3390/w15091693
- Sensitivity analysis of the hyperparameters of an ensemble Kalman filter application on a semi-distributed hydrological model for streamflow forecasting B. Sabzipour et al. 10.1016/j.jhydrol.2023.130251
- Multi-objective ensembles of echo state networks and extreme learning machines for streamflow series forecasting V. Ribeiro et al. 10.1016/j.engappai.2020.103910
Latest update: 04 Nov 2024
Short summary
Hydrological forecasting allows hydropower system operators to make the most efficient use of the available water as possible. Accordingly, hydrologists have been aiming at improving the quality of these forecasts. This work looks at the impacts of improving systematic errors in a forecasting scheme on the hydropower generation using a few decision-aiding tools that are used operationally by hydropower utilities. We find that the impacts differ according to the hydropower system characteristics.
Hydrological forecasting allows hydropower system operators to make the most efficient use of...
