แจ้งเอกสารไม่ครบถ้วน, ไม่ตรงกับชื่อเรื่อง หรือมีข้อผิดพลาดเกี่ยวกับเอกสาร ติดต่อที่นี่ ==>
หากไม่มีอีเมลผู้รับให้กรอก thailis-noc@uni.net.th
หากไม่มีอีเมลผู้รับให้กรอก thailis-noc@uni.net.th
Al-Masood, Nahid. Highly efficient short-term load forecasting scheme using long short-term memory network. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2020.
Highly efficient short-term load forecasting scheme using long short-term memory network
Name: Rafi, Shafiul Hasan
Organization :
Institute of Science and Technology. Dept. of Electrical, Electronic and Communication Engineering Military
Email :
shafiul.hasan1991@gmail.com
Name: Al-Masood, Nahid
Organization :
Bangladesh University of Engineering and Technology. Dept. of Electrical and Electronic Engineering
Email :
nahid.al.masood@gmail.com
keyword:
Short-term load forecasting
LCSH:
Electric power systems
Abstract:
Load forecasting procedure is one of the most
vital factors to make the selection of unit commitment,
economic dispatch, relaxation, spinning reserve capacity and
schedule device maintenance. Energy providers, related
organizations in power generation, transmission system and
policy makers of a country are dependent on the scale of future
electricity demand. Short term load forecasting (STLF)
procedure is considered one of the most complex and accuracy
defining factors of the electrical power system. However,
conventional forecasting strategies may not always fulfill the
precise result demand in the issue of STLF. In this research
work, a viable strategy for the issue of STLF procedure using
long short term memory network (LSTM), a special type of
recurrent neural network is presented. The forecasting
outcomes on time series electrical load data of Bangladesh
represent that the developed strategy has the ability to obtain
higher precision and accuracy in STLF. Evaluation parameters
of proposed network are also compared with the outcomes of
conventional regression model and obtained superior outputs.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2020
Modified:
2026-01-16
Issued:
2026-01-16
บทความ/Article
application/pdf
BibliograpyCitation :
In Electrical Engineering Academic Association (Thailand). 2020 8th International Electrical Engineering Congress (iEECON 2020) (pp.306-309). Red Hook, NY : Institute of Electrical and Electronics Engineers
eng
©copyrights King Mongkut's University of Technology North Bangkok
RightsAccess:
ใช้เวลา
0.036299 วินาที
Rafi, Shafiul Hasan
| Title | Contributor | Type |
|---|---|---|
| Highly efficient short-term load forecasting scheme using long short-term memory network
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Rafi, Shafiul Hasan;Al-Masood, Nahid | บทความ/Article |
Al-Masood, Nahid
| Title | Contributor | Type |
|---|---|---|
| Highly efficient short-term load forecasting scheme using long short-term memory network
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Rafi, Shafiul Hasan;Al-Masood, Nahid | บทความ/Article |