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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
Current issue: May 2017
Next issue: Aug 2017
Avg review time: 77 days


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Broken Bar Fault Detection in IM Operating Under No-Load Condition, RELJIC, D., JERKAN, D., MARCETIC, D., OROS, D.
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LATEST NEWS

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Apr-04
We have the confirmation Advances in Electrical and Computer Engineering will be included in the EBSCO database.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

2017-Jan-30
We have the confirmation Advances in Electrical and Computer Engineering will be included in the Gale database.

2016-Dec-17
IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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  1/2005 - 2

Reservoir Inflow Forecast Using Neural Networks: A Case Study of Wangchu River of Bhutan

Jigme SINGYE, Katsumi MASUGATA, Murai TADAKUNI
 
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Not available online | Views: 721

Author keywords
reservoir inflow, neural network, hydro power

References keywords
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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

Abstract
Quick view
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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Copyright ©2001-2017
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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