A  Thesis Submitted in  Partial Fulfillment of  the  Requirements for the  Degree ofMasters of  Science in  Hydraulic and Hydropower Engineerin
Creators: name: Kefyalew Abera Fite
Publisher: name: National Academic Digital Repository of Ethiopia
Year: 2011

description: To  determine  the  optimal  operation  policy  and  management  of reservoir  systems,  accurate 
forecast  of  the  inflows  to  the  reservoir  are  very  crucial.  Therefore,  the  aim  of this  research 
is  to  propose  a  best  forecasting  model  that  can  efficiently  forecast  the  random  inflow  to 
GD-3  reservoir  and  integrate  it  to  its  operational  planning.  Resulting  forecast,  simulation 
techniques  and  planning  vary  with  the  system  purpose,  physical  characteristics,  and 
availability  of data.  This paper presented  an  Artificial Neural Network  (ANN)  approach  for 
reservoir  inflow  forecasting  and  Water Evaluation  and  Planning  System  (WEAP)  model for 
its  power  simulation;  using  daily  stream  flow  and  rainfall  data.  Radial  Basis  Function 
(RBF)  of  ANN  and  the  Multiple  Regression  models  were  used  to  compare  the  model 
output  and  the  correlations  between  estimators  were  also  examined.  In  developing the  ANN 
models,  different  networks  with  different  numbers  of neuron hidden  layers  were  evaluated. 
A total  of 26  years  of  historical  data were  used to  train  and  test the  networks.  The  optimum 
ANN  network  with  5  inputs,  5  neurons  in  two  hidden  layers  and  one  output  was  selected. 
To  evaluate  the  accuracy  of the  proposed  model,  the  Mean  Squared  Error  (MSE)  and  the 
Nash-Sutcliffe  Correlation  Coefficient  (NSE) were  employed.  The  network  was trained  and 
converged  at  MSE = 0. 064 by  using training  data  subjected  to  early  stopping  approach.  The 
network  could  forecast  the  cross  validation  data  set  with  the  accuracy  of  MSE  =  0.067 . 
Training  and  cross  validation  process  of the  best  model  developed  during  testing  showed 
the  correlation coefficient of 0.936 and  0 . 939 respectively  . 
The  Water  Evaluation  and  Planning  System  (WEAP)  was  to  set  the  operational  planning 
for  GD-3  Reservoir  as  a  reference  scenario  and  evaluate  the  effects  on  its  downstream 
water  resource  development  (two  run  of river  hydroplants  GD-5  and  GD-6)  following  the 
construction  of  GD-3  Dam.  In  the  simulations  using  WEAP,  a  monthly  averaged  flow  of 
about  266Mm
3 
is  continuously  released  to  produce  a  continuous  power  of  1632GWh/year 
for  95%  of the  time.  The  construction  of GD-3  dam  stabilizes  the  flow  of middle  Genale 
River  by  improving  the  flow  pattern;  adds  about  65m
3 
Is  to  the  dry  months  flow  (Dec  to 
March)  and  reduces  the  peak  flow  (Aug  to  Oct)  by  more  than  75m
3
/s  and  generated  a total 
energy  of  3389GWh/year  for  96%  of the  time  including  the  two  run  of  rivers.  Therefore 
this  study  justified  that  coupling  inflow  forecasting  and  operational  planning  of reservoirs 
is  very  crucial  in  deriving the  benefits  derived from  GD-3  Hydropower scheme. 
Keywords:  Genale Dawa, ANN,  Forecasting,  Reservoir Inflow,  Simulation,  WEAP