Enhanced Artificial Neural Network Inflow Forecasting Algorithm for Run-of-River Hydropower Plants
Publication: Journal of Water Resources Planning and Management
Volume 128, Issue 6
Abstract
An improved artificial neural network-based algorithm for short-term water inflow forecasting (STWIF) into run-of-river hydropower plants is presented. Deregulation of the electrical power industry and introduction of competition between power producers have prompted redefinition of their daily operational tasks. In a competitive market environment, accurate short-term production planning and profitable bidding strategies become an important issue, requiring water inflow forecasts for up to 36 h ahead. As a result, short forecasting horizons have been found to be a main drawback of first-generation STWIF. An additional module using forecast precipitation data is developed for enlarging the forecast horizon up to two days ahead. The water inflow forecaster is further enhanced by inclusion of new input variables. With a large number of potential input variables, a new algorithm for selection of input variables using average mutual information and nonparametric density estimation is applied to the specific problem of water inflow forecasting. The performance of the enhanced STWIF is applied to the Soca River cascade hydropower system in Slovenia, and results are presented along with some comparisons with the previous-generation STWIF.
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Published In
Journal of Water Resources Planning and Management
Volume 128 • Issue 6 • November 2002
Pages: 415 - 423
Copyright
Copyright © 2002 American Society of Civil Engineers.
History
Received: Jun 4, 2001
Accepted: Nov 13, 2001
Published online: Oct 15, 2002
Published in print: Nov 2002
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