Case study of short-term electricity load forecasting with temperature dependency / (Record no. 2569)

MARC details
000 -LEADER
fixed length control field 01915nam a2200253 a 4500
001 - CONTROL NUMBER
control field vtls000055190
003 - CONTROL NUMBER IDENTIFIER
control field KUKTEM
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20251114204452.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 110804t2009 my a f m 000 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number THE0006681(Local)
039 #9 - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE]
Level of rules in bibliographic description 201905171506
Level of effort used to assign nonsubject heading access points amirul
-- 201108041004
-- Fida
040 ## - CATALOGING SOURCE
Original cataloging agency UMP
090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) TK1005 .T35 2009 rs Bc.
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Tai, Hein Fong
245 10 - TITLE STATEMENT
Title Case study of short-term electricity load forecasting with temperature dependency /
Statement of responsibility, etc. Tai Hein Fong
246 3# - VARYING FORM OF TITLE
Title proper/short title Case study of short-term electricity load forecasting with temperature dependency
Medium [computer file]
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Kuantan, Pahang :
Name of publisher, distributor, etc. UMP,
Date of publication, distribution, etc. 2009
300 ## - PHYSICAL DESCRIPTION
Extent xiv, 112 p. :
Other physical details ill. (some col.) ;
Dimensions 30 cm. +
Accompanying material 1 computer disc
502 ## - DISSERTATION NOTE
Dissertation note Project paper (Bachelor of Electrical Engineering (Power System)) -- Universiti Malaysia Pahang - 2009
520 3# - SUMMARY, ETC.
Summary, etc. Load forecasting is very essential to the operation of electricity companies. It enhances the energy-efficient and reliable operation of a power system. This is a case study of short-term load forecasting using Artificial Neural Networks (ANNs). This load forecasting program gives load forecasts half an hour in advance. Historical load data obtained from the electricity generation company will be use. The main stages are the pre-processing of the data sets, network training, and forecasting. The inputs used for the neural network are one set of historical load demand data and five sets of temperature data. The neural network used has 3 layers: an input, a hidden, and an output layer. The input layer has 5 neurons, the number of hidden layer neurons can be varied for the different performance of the network, while the output layer has a single neuron.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Electric power-plants
General subdivision Load
-- Forecasting
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Electric power systems
Holdings
Withdrawn status Lost status Damaged status Not for loan Home library Current library Date acquired Total checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
  Not lost   Not for loan UMPLIB PEKAN UMPLIB PEKAN 04/09/2019   TK1005 .T35 2009 rs Bc. 0000058382 04/09/2019 1 04/09/2019 Final Year Report
  Not lost   Not for loan UMPLIB PEKAN UMPLIB PEKAN 04/09/2019   CD 5372 | TK1005 .T35 2009 rs Bc. 0000058383 04/09/2019 1 04/09/2019 Final Year Report

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