Short-Term Load Forecasting by Artificial Intelligent Technologies

Dublin Core

Title

Short-Term Load Forecasting by Artificial Intelligent Technologies

Subject

Computer Science

Description

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).

Creator

Wei-Chiang Hong (Ed.) --- Ming-Wei Li (Ed.) --- Guo-Feng Fan (Ed.)

Source

https://www.mdpi.com/books/pdfview/book/1116

Publisher

MDPI - Multidisciplinary Digital Publishing Institute

Date

2019

Contributor

Baihaqi

Rights

Creatiev Commons

Format

PDF

Language

English

Type

Textbooks

Files

Citation

Wei-Chiang Hong (Ed.) --- Ming-Wei Li (Ed.) --- Guo-Feng Fan (Ed.), “Short-Term Load Forecasting by Artificial Intelligent Technologies,” Open Educational Resource (OER) - Unsyiah Library, accessed October 2, 2022, http://uilis.unsyiah.ac.id/oer/items/show/4895.

Document Viewer