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PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:Asia/Dhaka
BEGIN:DAYLIGHT
DTSTART:20380119T091407
TZOFFSETFROM:+0600
TZOFFSETTO:+0600
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DTSTART:20091231T230000
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BEGIN:VEVENT
DTSTAMP:20210831T124744Z
UID:C3B5C319-D7BB-43ED-A47C-42A8D2F42EEF
DTSTART;TZID=Asia/Dhaka:20210123T140300
DTEND;TZID=Asia/Dhaka:20210123T155400
DESCRIPTION:On 23rd January\, 2021\, IEEE BUBT Student Branch had arranged 
 a seminar on Power Forecasting Using Deep Learning Algorithm. The purpose 
 of the seminar was to learn deep learning algorithm for power forecasting.
  In this seminar\, our mentor was Dr. Abdul Motin Howlader. He had nicely 
 presented the deep learning algorithm. At first\, he was talking about the
  basic knowledge of power forecasting. He was talking about background of 
 the renewable energy\, importance of solar forecasting. Then he deep down 
 into the network algorithms and talked about the machine learning process.
  And took a broad class about the algorithm.\n\nPower forecasting is a tec
 hnique to predict future energy needs to achieve demand and supply equilib
 rium. Power forecasting of renewable energy power plants is a very active 
 research field\, as reliable information about the future power generation
  allows for a safe operation of the power grid and helps to minimize the o
 perational costs of these energy sources. Deep Learning algorithms have sh
 own to be very powerful in forecasting tasks\, such as economic time serie
 s or speech recognition. Up to now\, Deep Learning algorithms have only be
 en applied sparsely for forecasting renewable energy power plants. By usin
 g different Deep Learning and Artificial Neural Network algorithms\, such 
 as Deep Belief Networks\, Autoencoder\, and LSTM\, we introduce these powe
 rful algorithms in the field of renewable energy power forecasting. After 
 a short discuss the seminar was over. E-certificate was given to the activ
 e participants.\n\nThe You Tube link of this seminar: https://youtu.be/22Y
 5iwaYwQs\n\nVirtual: https://events.vtools.ieee.org/m/266381
LOCATION:Virtual: https://events.vtools.ieee.org/m/266381
ORGANIZER:ahtabil53@gmail.com
SEQUENCE:3
SUMMARY:Power Forecasting Using Deep Learning Algorithm
URL;VALUE=URI:https://events.vtools.ieee.org/m/266381
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;On 23&lt;sup&gt;rd&lt;/sup&gt; January\, 2021\, IEEE B
 UBT Student Branch had arranged a seminar on Power Forecasting Using Deep 
 Learning Algorithm. The purpose of the seminar was to learn deep learning 
 algorithm for power forecasting. In this seminar\, our mentor was Dr. Abdu
 l Motin Howlader. He had nicely presented the deep learning algorithm. At 
 first\, he was talking about the basic knowledge of power forecasting. He 
 was talking about background of the renewable energy\, importance of solar
  forecasting. Then he deep down into the network algorithms and talked abo
 ut the machine learning process. And took a broad class about the algorith
 m.&lt;/p&gt;\n&lt;p&gt;Power forecasting is a technique to predict future energy needs
  to achieve demand and supply equilibrium. Power forecasting of renewable 
 energy power plants is a very active research field\, as reliable informat
 ion about the future power generation allows for a safe operation of the p
 ower grid and helps to minimize the operational costs of these energy sour
 ces. Deep Learning algorithms have shown to be very powerful in forecastin
 g tasks\, such as economic time series or speech recognition. Up to now\, 
 Deep Learning algorithms have only been applied sparsely for forecasting r
 enewable energy power plants. By using different Deep Learning and Artific
 ial Neural Network algorithms\, such as Deep Belief Networks\, Autoencoder
 \, and LSTM\, we introduce these powerful algorithms in the field of renew
 able energy power forecasting. After a short discuss the seminar was over.
  E-certificate was given to the active participants.&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;The Y
 ou Tube link of this seminar&lt;/strong&gt;&lt;strong&gt;:&lt;/strong&gt;&lt;u&gt; &lt;/u&gt;&lt;a href=&quot;ht
 tps://youtu.be/22Y5iwaYwQs&quot;&gt;https://youtu.be/22Y5iwaYwQs&lt;/a&gt;&lt;/p&gt;
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