Next Article in Journal
Mathematical Modeling of the Biogas Production in MSW Landfills. Impact of the Implementation of Organic Matter and Food Waste Selective Collection Systems
Next Article in Special Issue
Application of a Novel Hybrid Wavelet-ANFIS/Fuzzy C-Means Clustering Model to Predict Groundwater Fluctuations
Previous Article in Journal
Explicit Modeling of Meteorological Explanatory Variables in Short-Term Forecasting of Maximum Ozone Concentrations via a Multiple Regression Time Series Framework
Previous Article in Special Issue
Prediction of Short-Time Cloud Motion Using a Deep-Learning Model
Article

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

1
Department of Risk Assessment and Adaptation Strategies, Ca’Foscari University of Venice, 30123 Venezia (VE), Italy
2
Euro-Mediterranean Center on Climate Change, 30175 Venezia Marghera (VE), Italy
3
GECOsistema Srl, 47521 Cesena, Italy
4
Enel Green Power S.p.A., 00198 Rome, Italy
*
Author to whom correspondence should be addressed.
Atmosphere 2020, 11(12), 1305; https://doi.org/10.3390/atmos11121305
Received: 28 October 2020 / Revised: 27 November 2020 / Accepted: 30 November 2020 / Published: 1 December 2020
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. View Full-Text
Keywords: climate service; hydropower; machine learning; water resources management; seasonal forecasting climate service; hydropower; machine learning; water resources management; seasonal forecasting
Show Figures

Figure 1

MDPI and ACS Style

Essenfelder, A.H.; Larosa, F.; Mazzoli, P.; Bagli, S.; Broccoli, D.; Luzzi, V.; Mysiak, J.; Mercogliano, P.; dalla Valle, F. Smart Climate Hydropower Tool: A Machine-Learning Seasonal Forecasting Climate Service to Support Cost–Benefit Analysis of Reservoir Management. Atmosphere 2020, 11, 1305. https://doi.org/10.3390/atmos11121305

AMA Style

Essenfelder AH, Larosa F, Mazzoli P, Bagli S, Broccoli D, Luzzi V, Mysiak J, Mercogliano P, dalla Valle F. Smart Climate Hydropower Tool: A Machine-Learning Seasonal Forecasting Climate Service to Support Cost–Benefit Analysis of Reservoir Management. Atmosphere. 2020; 11(12):1305. https://doi.org/10.3390/atmos11121305

Chicago/Turabian Style

Essenfelder, Arthur H.; Larosa, Francesca; Mazzoli, Paolo; Bagli, Stefano; Broccoli, Davide; Luzzi, Valerio; Mysiak, Jaroslav; Mercogliano, Paola; dalla Valle, Francesco. 2020. "Smart Climate Hydropower Tool: A Machine-Learning Seasonal Forecasting Climate Service to Support Cost–Benefit Analysis of Reservoir Management" Atmosphere 11, no. 12: 1305. https://doi.org/10.3390/atmos11121305

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop