scholarly journals Pattern Matching and Neural Networks based Hybrid Forecasting System

2001 ◽  
pp. 72-82 ◽  
Author(s):  
Sameer Singh ◽  
Jonathan Fieldsend
Keyword(s):  
Neural Networks ◽  
Pattern Matching ◽  
Hybrid Forecasting ◽  
Forecasting System

An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

2017 ◽  
Vol 7 (9) ◽  
pp. 13
Author(s):  
Bhargavi Munnaluri ◽  
K. Ganesh Reddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Support Vector ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed.  In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

scholarly journals The development of the water flow forecasting system into the dam by neural networks and fuzzy theory

1999 ◽  
Vol 119 (10) ◽  
pp. 1020-1025 ◽  
Author(s):  
Tatsuya Iizaka ◽  
Tetsuro Matsui ◽  
Yoshiteru Ueki
Keyword(s):  
Neural Networks ◽  
Water Flow ◽  
Fuzzy Theory ◽  
Forecasting System

scholarly journals Improving statistical forecasts of seasonal streamflows using hydrological model output

2012 ◽  
Vol 9 (7) ◽  
pp. 8701-8736 ◽  
Author(s):  
D. E. Robertson ◽  
P. Pokhrel ◽  
Q. J. Wang
Keyword(s):  
Hydrological Model ◽  
Historical Record ◽  
Base Flow ◽  
Seasonal Forecasting ◽  
Streamflow Forecasting ◽  
Eastern Australia ◽  
True Source ◽  
Hybrid Forecasting ◽  
Forecasting System ◽  
Seasonal Streamflow

Abstract. Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1) when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2) when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3) when the initial catchment condition is near saturation intermittently throughout the historical record.

An online air pollution forecasting system using neural networks

2008 ◽  
Vol 34 (5) ◽  
pp. 592-598 ◽  
Author(s):  
Atakan Kurt ◽  
Betul Gulbagci ◽  
Ferhat Karaca ◽  
Omar Alagha
Keyword(s):  
Neural Networks ◽  
Air Pollution ◽  
Forecasting System ◽  
Air Pollution Forecasting
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