Short term load forecasting by statistical time series methods
Joint Authors
Source
Journal of Automation and Systems Engineering
Issue
Vol. 10, Issue 2 (30 Jun. 2016), pp.99-111, 13 p.
Publisher
Publication Date
2016-06-30
Country of Publication
Algeria
No. of Pages
13
Main Subjects
Abstract EN
The electricity demand forecasting is a pioneer study in the field of electrical engineering.
Demand forecasting is inevitable for impeccable operation of the power system on the other hand it is required for long term planning.
Recent year’s development of new methodologies for Short Term Load Forecasting (STLF) has gain the interest of researchers.
STLF is required for fixing the bidding strategies, strategic decisions and generator scheduling.
Early information of demand can be a beneficial tool for energy management centre.
In view of this light, this paper presents an application of statistical forecasting method for predicting the demand on hourly basis.
On the basis of average and peak demand the similar days are selected and forecast for next hours are carried out.
The paper also presents a meaningful comparison of different statistical load forecasting methods namely trend analysis, decomposition and moving average method.
It is observed that the moving method outperformed over other methods.
American Psychological Association (APA)
Mittal, Nikita& Saxena, Akash. 2016. Short term load forecasting by statistical time series methods. Journal of Automation and Systems Engineering،Vol. 10, no. 2, pp.99-111.
https://search.emarefa.net/detail/BIM-748197
Modern Language Association (MLA)
Mittal, Nikita& Saxena, Akash. Short term load forecasting by statistical time series methods. Journal of Automation and Systems Engineering Vol. 10, no. 2 (2016), pp.99-111.
https://search.emarefa.net/detail/BIM-748197
American Medical Association (AMA)
Mittal, Nikita& Saxena, Akash. Short term load forecasting by statistical time series methods. Journal of Automation and Systems Engineering. 2016. Vol. 10, no. 2, pp.99-111.
https://search.emarefa.net/detail/BIM-748197
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 111
Record ID
BIM-748197