Essenfelder, AH;
Larosa, F;
Mazzoli, P;
Bagli, S;
Broccoli, D;
Luzzi, V;
Mysiak, J;
... Dalla Valle, F; + view all
(2020)
Smart Climate Hydropower Tool: A Machine-Learning Seasonal Forecasting Climate Service to Support Cost–Benefit Analysis of Reservoir Management.
Atmosphere
, 11
(12)
, Article 1305. 10.3390/atmos11121305.
Preview |
Text
atmosphere-11-01305.pdf - Published Version Download (8MB) | Preview |
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.
| Type: | Article |
|---|---|
| Title: | Smart Climate Hydropower Tool: A Machine-Learning Seasonal Forecasting Climate Service to Support Cost–Benefit Analysis of Reservoir Management |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3390/atmos11121305 |
| Publisher version: | https://doi.org/10.3390/atmos11121305 |
| Language: | English |
| Additional information: | This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | climate service; hydropower; machine learning; water resources management; seasonal forecasting |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10119497 |
Archive Staff Only
 |
View Item |
