Smart Climate Hydropower Tool: A Machine-Learning Seasonal Forecasting Climate Service to Support Cost–Benefit Analysis of Reservoir Management

This study proposes a climate service named Smart Climate Hydropower Tool (SCHT) and designed as a hybrid forecast system for supporting decision-making in a context of hydropower production. SCHT is technically designed to make use of information from state-of-art seasonal forecasts provided by the Copernicus Climate Data Store (CDS) combined with a range of different machine learning algorithms to perform the seasonal forecast of the accumulated inflow discharges to the reservoir of hydropower plants. The machine learning algorithms considered include support vector regression, Gaussian processes, long short-term memory, non-linear autoregressive neural networks with exogenous inputs, and a deep-learning neural networks model. Each machine learning model is trained over past decades datasets of recorded data, and forecast performances are validated and evaluated using separate test sets with reference to the historical average of discharge values and simpler multiparametric regressions. Final results are presented to the users through a user-friendly web interface developed from a tied connection with end-users in an effective co-design process. Methods are tested for forecasting the accumulated seasonal river discharges up to six months in advance for two catchments in Colombia, South America. Results indicate that the machine learning algorithms that make use of a complex and/or recurrent architecture can better simulate the temporal dynamic behaviour of the accumulated river discharge inflow to both case study reservoirs, thus rendering SCHT a useful tool in providing information for water resource managers in better planning the allocation of water resources for different users and for hydropower plant managers when negotiating power purchase contracts in competitive energy markets.

[1]  E. Wood,et al.  The role of initial conditions and forcing uncertainties in seasonal hydrologic forecasting , 2009 .

[2]  R. Schaeffer,et al.  Energy sector vulnerability to climate change: A review , 2012 .

[3]  Enli Wang,et al.  Monthly and seasonal streamflow forecasts using rainfall‐runoff modeling and historical weather data , 2011 .

[4]  Tamás D. Gedeon,et al.  Data Mining of Inputs: Analysing Magnitude and Functional Measures , 1997, Int. J. Neural Syst..

[5]  Luis Berga,et al.  The Role of Hydropower in Climate Change Mitigation and Adaptation: A Review , 2016 .

[6]  T. Palmer,et al.  Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts , 2009 .

[7]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[8]  Faming Liang,et al.  Explicitly integrating parameter, input, and structure uncertainties into Bayesian Neural Networks for probabilistic hydrologic forecasting , 2011 .

[9]  Rob J. Hyndman,et al.  A note on the validity of cross-validation for evaluating autoregressive time series prediction , 2018, Comput. Stat. Data Anal..

[10]  Rebecca E. Morss,et al.  Communicating Uncertainty in Weather Forecasts: A Survey of the U.S. Public , 2008 .

[11]  A. Robertson,et al.  Evaluation of Submonthly Precipitation Forecast Skill from Global Ensemble Prediction Systems , 2015 .

[12]  Carlo Giupponi,et al.  A coupled hydrologic-machine learning modelling framework to support hydrologic modelling in river basins under Interbasin Water Transfer regimes , 2020, Environ. Model. Softw..

[13]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[14]  D. C. Sohoulande Djebou Streamflow Drought Interpreted Using SWAT Model Simulations of Past and Future Hydrologic Scenarios: Application to Neches and Trinity River Basins, Texas , 2019, Journal of Hydrologic Engineering.

[15]  Ibrahim Yuksel,et al.  Hydropower for sustainable water and energy development , 2010 .

[16]  F. Molteni,et al.  SEAS5: the new ECMWF seasonal forecast system , 2018, Geoscientific Model Development.

[17]  Vijay P. Singh,et al.  Seasonal Drought Prediction: Advances, Challenges, and Future Prospects , 2018 .

[18]  Verification of ECMWF System 4 for seasonal hydrological forecasting in a northern climate , 2017 .

[19]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[20]  Morten Hjorth-Jensen,et al.  Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model , 2020 .

[21]  Kiyotaka Tahara,et al.  Evaluation of CO2 payback time of power plants by LCA , 1997 .

[22]  Mark Z. Jacobson,et al.  Review of solutions to global warming, air pollution, and energy security , 2009 .

[23]  W. Collischonn,et al.  Evaluation of upper Uruguay river basin (Brazil) operational flood forecasts , 2017 .

[24]  P. Moorcroft,et al.  Impacts of climate change and deforestation on hydropower planning in the Brazilian Amazon , 2020, Nature Sustainability.

[25]  Indrajeet Chaubey,et al.  Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed , 2008 .

[26]  Paul Block,et al.  Tailoring seasonal climate forecasts for hydropower operations , 2011 .

[27]  Jeffrey G. Arnold,et al.  Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations , 2007 .

[28]  P. Jones,et al.  A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006 , 2008 .

[29]  S. Bagli,et al.  Operational River Discharge Forecasting with Support Vector Regression Technique Applied to Alpine Catchments: Results, Advantages, Limits and Lesson Learned , 2017, Water Resources Management.

[30]  C. J. Franco,et al.  Assessing security of supply in a largely hydroelectricity-based system: The Colombian case , 2018, Energy.

[31]  E. Wood,et al.  Seasonal Forecasting of Global Hydrologic Extremes: System Development and Evaluation over GEWEX Basins , 2015 .

[32]  James A. Edmonds,et al.  A Comprehensive View of Global Potential for Hydro-generated Electricity , 2015 .

[33]  C. Notarnicola,et al.  Seasonal river discharge forecasting using support vector regression: A case study in the Italian Alps , 2015 .

[34]  P. D. Heermann,et al.  Classification of multispectral remote sensing data using a back-propagation neural network , 1992, IEEE Trans. Geosci. Remote. Sens..

[35]  Carlos Jaime Franco,et al.  Analyzing the Hydroelectricity Variability on Power Markets from a System Dynamics and Dynamic Systems Perspective: Seasonality and ENSO Phenomenon , 2020 .

[36]  Karsten Schulz,et al.  Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks , 2018, Hydrology and Earth System Sciences.

[37]  F. Wolak,et al.  Retail Pricing in Colombia to Support the Efficient Deployment of Distributed Generation and Electric Vehicles , 2020, Journal of Environmental Economics and Management.

[38]  S. Galelli,et al.  Complex relationship between seasonal streamflow forecast skill and value in reservoir operations , 2017 .

[39]  Dennis P. Lettenmaier,et al.  Economic Value of Long-Lead Streamflow Forecasts for Columbia River Hydropower , 2002 .

[40]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[41]  Valentina Krysanova,et al.  How the performance of hydrological models relates to credibility of projections under climate change , 2018 .

[42]  F. Hossain,et al.  Maximizing energy production from hydropower dams using short-term weather forecasts , 2020 .

[43]  Bekir Z. Demiray,et al.  A comprehensive review of deep learning applications in hydrology and water resources. , 2020, Water science and technology : a journal of the International Association on Water Pollution Research.

[44]  Changbo Jiang,et al.  Seasonal Inflow Forecasts Using Gridded Precipitation and Soil Moisture Information: Implications for Reservoir Operation , 2019, Water Resources Management.

[45]  K. Stone,et al.  Climate-driven prediction of land water storage anomalies: An outlook for water resources monitoring across the conterminous United States , 2020 .

[46]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[47]  Caio A. S. Coelho,et al.  Assessment of ECMWF SEAS5 Seasonal Forecast Performance over South America , 2019, Weather and Forecasting.

[48]  Jaewook Lee,et al.  Clustering Based on Gaussian Processes , 2007, Neural Computation.

[49]  A. Soldati,et al.  Artificial neural network approach to flood forecasting in the River Arno , 2003 .

[50]  Frederic Vitart,et al.  Madden—Julian Oscillation prediction and teleconnections in the S2S database , 2017 .

[51]  Peggy A. Johnson,et al.  Stream hydrological and ecological responses to climate change assessed with an artificial neural network , 1996 .

[52]  Ligang Xu,et al.  Comparison of Long Short Term Memory Networks and the Hydrological Model in Runoff Simulation , 2020, Water.

[53]  Kurt Hornik,et al.  kernlab - An S4 Package for Kernel Methods in R , 2004 .

[54]  David E.H.J. Gernaat,et al.  High-resolution assessment of global technical and economic hydropower potential , 2017 .

[55]  Fabio Roli,et al.  Design of effective neural network ensembles for image classification purposes , 2001, Image Vis. Comput..

[56]  A. Castelletti,et al.  The Value of Subseasonal Hydrometeorological Forecasts to Hydropower Operations: How Much Does Preprocessing Matter? , 2019, Water Resources Research.