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

Abstract

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.

Evaluation of Earth Observations and In Situ Data Assimilation for Seasonal Hydrological Forecasting

“…Due to uncertainties from model errors and physical meteorological processes which increase with lead time, hydrologic forecast performance generally decreases with lead time. Long‐range forecasts are inherently uncertain but nonetheless useful to decision makers in for example, planning long‐term strategies for reservoir storage (Anghileri et al., 2016; Essenfelder et al., 2020) and supporting adaptation to climate change (White et al., 2021; Winsemius et al., 2014).…”

Thermal stress information as a tourism-oriented climate product: Performance analysis for selected urban destinations in Romania and Italy

“…European initiatives like ERA4CS [ 15 ], PLACARD [ 16 ], Copernicus Climate Change Services (C3S) [ 17 ] fostered the design of the research framework for the development of a large palette of climate products and services. Several services, offered by public or private entities, are available, targeting economic sectors like agriculture [ 18 ], energy [ 19 ], water management [ 20 ], risk management [ 21 ], urban climate resilience and adaptation [ 22 , 23 ].…”

Co-evaluation of climate services. A case study for hydropower generation

“…Here we implement the methodology to quantify the value of a hydropower generation related climate service: the "Smart Hydropower Climate Tool", hereto SCHT (www.https://gecosistema.com/climatetools/scht-smart-climate-hydropower-tool/ and Essenfelder et al, 2020) examined within the CLARA H2020 project (www.clara-project. eu).…”