Short-Term Load Forecasting by Artificial Intelligent Technologies

Short-Term Load Forecasting by Artificial Intelligent Technologies

Computer Science

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).

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

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

MDPI - Multidisciplinary Digital Publishing Institute

2019

Baihaqi

Creatiev Commons

PDF

English

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