ZONE STOCHASTIC FORECASTING MODEL FOR MANAGEMNT OF LARGE OPEN WATER RESERVOIR WITH STORAGE FUNCTION

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F16%3APU119729" target="_blank" >RIV/00216305:26110/16:PU119729 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.5593/SGEM2016/B31/S12.087" target="_blank" >http://dx.doi.org/10.5593/SGEM2016/B31/S12.087</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5593/SGEM2016/B31/S12.087" target="_blank" >10.5593/SGEM2016/B31/S12.087</a>

Jazyk výsledku

angličtina

Název v původním jazyce

ZONE STOCHASTIC FORECASTING MODEL FOR MANAGEMNT OF LARGE OPEN WATER RESERVOIR WITH STORAGE FUNCTION

Popis výsledku v původním jazyce

The main advantage of stochastic forecasting is fan of possible value, which deterministic method of forecasting could not give us. Future development of random process is described much better by stochastic then deterministic forecasting. We can categorize discharge in measurement profile as random process. Contents of article are development of forecasting model for managed large open water reservoir with supply function. Model is based on zone linear autoregressive model, which forecasting values of average monthly flow from linear combination previous values of average monthly flow, autoregressive coefficients and random numbers. All data were sorted to zone with same size (last zone has different size due to residue of data). Computing zone was chosen by last measurement average monthly flow. Matrix of correlation was assembled only from data belonging to matching zone. Autoregressive coefficient was calculated from Yule-Walker equations (Yule, Walker, 1927, 1931). The model was compiled for forecast of 1 to 12 month with backward correlation from 2 to 11 months. Data was got rid of asymmetry with help of Box-Cox rule (Box, Cox, 1964), value r was found by optimization. In next step were data transform to standard normal distribution. Our data were with monthly step and forecasting was recurrent. We used 90 years long real flow series for compile of the model. First 75 years were used for calibration of model (autoregressive coefficient), last 15 years were used only for validation. Outputs of model were compared with real flow series. For comparison between real flow series (100% successfully of forecast) and forecasts, we used histogram and average error between each forecasted flow and measurement flow. Results were statistically evaluated on monthly level. Results show that the longest backward correlation did not give the best results. Flows forecasted by the model give very fine results in drought period. Higher errors were reached in months with highe

Název v anglickém jazyce

ZONE STOCHASTIC FORECASTING MODEL FOR MANAGEMNT OF LARGE OPEN WATER RESERVOIR WITH STORAGE FUNCTION

Popis výsledku anglicky

The main advantage of stochastic forecasting is fan of possible value, which deterministic method of forecasting could not give us. Future development of random process is described much better by stochastic then deterministic forecasting. We can categorize discharge in measurement profile as random process. Contents of article are development of forecasting model for managed large open water reservoir with supply function. Model is based on zone linear autoregressive model, which forecasting values of average monthly flow from linear combination previous values of average monthly flow, autoregressive coefficients and random numbers. All data were sorted to zone with same size (last zone has different size due to residue of data). Computing zone was chosen by last measurement average monthly flow. Matrix of correlation was assembled only from data belonging to matching zone. Autoregressive coefficient was calculated from Yule-Walker equations (Yule, Walker, 1927, 1931). The model was compiled for forecast of 1 to 12 month with backward correlation from 2 to 11 months. Data was got rid of asymmetry with help of Box-Cox rule (Box, Cox, 1964), value r was found by optimization. In next step were data transform to standard normal distribution. Our data were with monthly step and forecasting was recurrent. We used 90 years long real flow series for compile of the model. First 75 years were used for calibration of model (autoregressive coefficient), last 15 years were used only for validation. Outputs of model were compared with real flow series. For comparison between real flow series (100% successfully of forecast) and forecasts, we used histogram and average error between each forecasted flow and measurement flow. Results were statistically evaluated on monthly level. Results show that the longest backward correlation did not give the best results. Flows forecasted by the model give very fine results in drought period. Higher errors were reached in months with highe

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

SGEM Conference Proceedingsc

ISBN

978-619-7105-61-2

ISSN

1314-2704

e-ISSN

—

Počet stran výsledku

6

Strana od-do

555-561

Název nakladatele

STEF92 Technology Ltd.

Místo vydání

51 Alexander Malinov Blvd., 1712, Sofia, Bulgari

Místo konání akce

Albena

Datum konání akce

30. 6. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000391653400110