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Short term load forecastiong using time seasonal autoregressive integrated moving average

Wan Abdul Razak, Intan Azmira and Majid, Md. Shah and Abd. Rahman, Hasimah and Hassan, Mohammad Yusri (2007) Short term load forecastiong using time seasonal autoregressive integrated moving average. In: Recent Trends In Power System Operation. Penerbit UTM , Johor, pp. 16-39. ISBN 978-983-52-0681-8

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Abstract

A power delivery system exists because consumers want electric power. With supply and demand fluctuating and the changes of weather conditions and energy prices increasing during peak situations, load forecasting is vitally important for utilities. Electric load forecasting can be divided into three categories that are short term load forecasting, medium term load forecasting and long term load forecasting. The short term load forecasting predicts the load demand in time interval from one day to several weeks. It can help to estimate load flows and to make decisions that can prevent overloading. Therefore it leads to the improvement of network reliability and reduces occurrences of equipment failures and blackouts. The medium term load forecasting predicts the load demand from a month to several years.

Item Type:Book Section
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:14061
Deposited By: Mrs Liza Porijo
Deposited On:17 Aug 2011 07:55
Last Modified:17 Aug 2011 07:55

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