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Reservoir Inflow Forecast Using Neural Networks: A Case Study of Wangchu River of BhutanJigme SINGYE, Katsumi MASUGATA, Murai TADAKUNI
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reservoir inflow, neural network, hydro power
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About this article
Date of Publication: 2005-04-02
Volume 5, Issue 1, Year 2005, On page(s): 10 - 16
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: Not assigned
Efficient river inflow forecast is essential for various purposes such as for flood control, distribution of water for irrigation purposes, etc. It is also a highly useful tool for the efficient operation and maintenance of hydro power plants as the economics of the hydro electricity depend largely on the reservoir height as well as the inflow rate into the dam. Through effective inflow prediction mechanism, proper maintenance and operation schedule of the generating machines can be planned, thereby reducing the forced outages and increasing the generation output. In this paper, a neural network based approach is presented to forecast the daily river inflow for a Wangchu River of Bhutan since on this river basin lie the two biggest hydro plants, Chukha Hydropower Corporation (CHPC) and Tala Hydro Project Authority (THPA), with generating capacities of 336 MW and 1020 MW, respectively. For a small Himalayan kingdom where half the total national revenue comes from the sale of hydro power alone, with almost around 80 percent exported to India, it is essential to have a proper mechanism to accurately forecast the inflow as it would help implement the optimal utilization of water resources as well as plan efficient load scheduling. The latter is particularly important for power export as prior electric generation anticipation based on river inflow is critical in transacting the power sale in advance, thereby significantly improving the revenue earned. In this paper, two types of neural networks are designed and their performances compared in predicting the next day inflow. Both the networks are extensively tested using the inflow and other weather related data from the year 1999 to 2003.
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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