Inflow forecasting using Artificial Neural Networks for reservoir operation
In this study, multi-layer perceptron (MLP) artificial neural networks have been applied to forecast one-month-ahead inflow for the Ubonratana reservoir, Thailand. To assess how well the forecast inflows have performed in the operation of the reservoir, simulations were carried out guided by the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-05-01
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| Series: | Proceedings of the International Association of Hydrological Sciences |
| Online Access: | https://www.proc-iahs.net/373/209/2016/piahs-373-209-2016.pdf |
| Summary: | In this study, multi-layer perceptron (MLP) artificial neural networks have
been applied to forecast one-month-ahead inflow for the Ubonratana reservoir,
Thailand. To assess how well the forecast inflows have performed in the
operation of the reservoir, simulations were carried out guided by the
systems rule curves. As basis of comparison, four inflow situations were
considered: (1) inflow known and assumed to be the historic (Type A);
(2) inflow known and assumed to be the forecast (Type F); (3) inflow known
and assumed to be the historic mean for month (Type M); and (4) inflow is
unknown with release decision only conditioned on the starting reservoir
storage (Type N). Reservoir performance was summarised in terms of
reliability, resilience, vulnerability and sustainability. It was found that
Type F inflow situation produced the best performance while Type N was the
worst performing. This clearly demonstrates the importance of good inflow
information for effective reservoir operation. |
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| ISSN: | 2199-8981 2199-899X |