Applying wavelet transformation and artificial neural networks to develop forecasting-based reservoir operating rule curves

2020 ◽  
Vol 65 (12) ◽  
pp. 2007-2021
Author(s):  
Seyed Mohammad Ashrafi ◽  
Ehsan Mostaghimzadeh ◽  
Arash Adib
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wavelet Transformation ◽  
Rule Curves ◽  
Artificial Neural ◽  
Operating Rule ◽  
Operating Rule Curves

scholarly journals Inflow forecasting using Artificial Neural Networks for reservoir operation

2016 ◽  
Vol 373 ◽  
pp. 209-214 ◽  
Author(s):  
Chuthamat Chiamsathit ◽  
Adebayo J. Adeloye ◽  
Soundharajan Bankaru-Swamy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reservoir Operation ◽  
Multi Layer Perceptron ◽  
Reservoir Performance ◽  
Reservoir Simulations ◽  
Rule Curves ◽  
Inflow Forecasting ◽  
Artificial Neural ◽  
Storage Type

Abstract. 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.

Sign in / Sign up

Export Citation Format

Share Document