Short term load forecasting for system control & operational planning

Pao Yue-kong Library Electronic Theses Database

Short term load forecasting for system control & operational planning


Author: Fung, Tze-chiu
Title: Short term load forecasting for system control & operational planning
Degree: M.Sc.
Year: 1997
Subject: Electric power-plants -- Load -- Forecasting
Electric power-plants -- Load -- Mathematical models
Neural networks (Computer science)
Electric power consumption -- Forecasting
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Pages: vii, 72 leaves : ill. ; 30 cm
Language: English
InnoPac Record:
Abstract: Short Term Load Forecasting is one of the principal elements in power system operation. It is required for unit commitment, energy transfer scheduling, load dispatch and security assessment. Accuracy in load forecasting is a crucial task in generation cost optimisation. This project presents an artificial neural network (ANN) based Short Term Load Forecasting Model. The model is developed to meet the needs and to cope with the environment of the supply utilities in Hong Kong. Four different categories of models (Weekday, Saturday, Sunday and Holiday) are proposed. Detailed study is carried out in terms of training and testing the neural nets with historical power utility data and weather data. A sufficient number of effective learning data are selected by taking advantage of the expert knowledge of the experienced utility engineers. The forecasting model consists of two part: 1) off line neural network models for learning and testing, and 2) on line models for engineers in daily load prediction. The developed model is applied to load forecasting of a power utility in Hong Kong for field test. As far as the objective of introducing automation to the load forecasting task is concerned, this project is considered a successful attempt.

Files in this item

Files Size Format
b1437013x.pdf 2.509Mb PDF
Copyright Undertaking
As a bona fide Library user, I declare that:
  1. I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
  2. I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
  3. I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.
By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.


Quick Search


More Information