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Short-Term Load Forecasting by Artificial Intelligent Technologies

In last few decades, short-term load forecasting (STLF) has been one of the most important research issues for achieving higher efficiency and reliability in power system operation, to facilitate the minimization of its operation cost by providing accurate input to day-ahead scheduling, contingency analysis, load flow analysis, planning, and maintenance of power systems. There are lots of forecasting models proposed for STLF, including traditional statistical models (such as ARIMA, SARIMA, ARMAX, multi-variate regression, Kalman filter, exponential smoothing, and so on) and artificial-intelligence-based models (such as artificial neural networks (ANNs), knowledge-based expert systems, fuzzy theory and fuzzy inference systems, evolutionary computation models, support vector regression, and so on).

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Detail Information
Series Title
-
Call Number
003.2 HON s
Publisher
: MDPI - Multidisciplinary Digital Publishing Institute.,
Collation
-
Language
English
ISBN/ISSN
978-3-03897-583-0
Classification
003.2
Content Type
text
Media Type
computer
Carrier Type
online resource
Edition
-
Subject(s)
Computer Science
Specific Detail Info
-
Statement of Responsibility
Other Information
Cataloger
Kurnadi
Source
-
Validator
maya
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No other version available

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