Title :
Application of artificial neural network to forecasting methods of time variation of the flow rate into a dam for a hydro-power plant
Author :
Ichiyanagi, K. ; Kobayashi, H. ; Matsumura, T. ; Kito, Y.
Author_Institution :
Dept. of Electr. Eng., Aichi Inst. of Technol., Toyota, Japan
Abstract :
This paper describes an attempt to apply a neural network method to forecast river flow rate following a fall of rain. The authors use a perceptron-type network comprised of three layers. The input data to the neural network are rainfall amounts and subsequent river flow rates. Further the predicted total volume and duration of the spell of rainfall in question are taken as additional input data. The output from the neural network is forecasted river flow rate. It is found from these investigations that the forecasting accuracy of the neural network is improved by utilization of the linear input-output relations of neurons.
Keywords :
dams; geophysics computing; hydroelectric power stations; hydrological techniques; neural nets; power engineering computing; rain; rivers; water supply; accuracy; artificial neural network; dam; hydroelectric power stations; hydrology; linear input-output relations; perceptron-type network; rain; river flow rate; three layers; Artificial neural networks; Computer networks; Gases; Load forecasting; Neural networks; Neurons; Rain; Rivers; Technology forecasting; Transfer functions;
Conference_Titel :
Neural Networks to Power Systems, 1993. ANNPS '93., Proceedings of the Second International Forum on Applications of
Conference_Location :
Yokohama, Japan
Print_ISBN :
0-7803-1217-1
DOI :
10.1109/ANN.1993.264323