DOI: 10.5194/hess-19-3695-2015
Scopus记录号: 2-s2.0-84940861028
论文题名: Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework
作者: Gragne A ; S ; , Sharma A ; , Mehrotra R ; , Alfredsen K
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2015
卷: 19, 期: 8 起始页码: 3695
结束页码: 3714
语种: 英语
Scopus关键词: Catchments
; Commerce
; Decision making
; Hydroelectric power
; Reservoirs (water)
; Uncertainty analysis
; Water resources
; Complementary modelling
; Decision making process
; Forecasting modeling
; Hydro-power generation
; Hydropower reservoirs
; Probabilistic evaluation
; Probabilistic inflow
; Real-time forecasting
; Forecasting
; accuracy assessment
; complementarity
; decision making
; forecasting method
; hydroelectric power plant
; hydrological modeling
; inflow
; reservoir
; time series
; Norway
; Scandinavia
英文摘要: Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead time is considered within the day-ahead (Elspot) market of the Nordic exchange market. A complementary modelling framework presents an approach for improving real-time forecasting without needing to modify the pre-existing forecasting model, but instead formulating an independent additive or complementary model that captures the structure the existing operational model may be missing. We present here the application of this principle for issuing improved hourly inflow forecasts into hydropower reservoirs over extended lead times, and the parameter estimation procedure reformulated to deal with bias, persistence and heteroscedasticity. The procedure presented comprises an error model added on top of an unalterable constant parameter conceptual model. This procedure is applied in the 207 km2 Krinsvatn catchment in central Norway. The structure of the error model is established based on attributes of the residual time series from the conceptual model. Besides improving forecast skills of operational models, the approach estimates the uncertainty in the complementary model structure and produces probabilistic inflow forecasts that entrain suitable information for reducing uncertainty in the decision-making processes in hydropower systems operation. Deterministic and probabilistic evaluations revealed an overall significant improvement in forecast accuracy for lead times up to 17 h. Evaluation of the percentage of observations bracketed in the forecasted 95 % confidence interval indicated that the degree of success in containing 95 % of the observations varies across seasons and hydrologic years. © Author(s) 2015. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78436
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Hydraulic and Environmental Engineering, Norwegian University of Science and Technology, Trondheim, Norway; School of Civil and Environmental Engineering, University of New South Wales, Sydney, Australia
Recommended Citation:
Gragne A,S,, Sharma A,et al. Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework[J]. Hydrology and Earth System Sciences,2015-01-01,19(8)