Short-Term Load Forecasting by Artificial Intelligent Technologies

Buch | Softcover

444 Seiten

2019
MDPI (Verlag)
9783038975823 (ISBN)

Keine Verlagsinformationen verfügbar

Artikel merken

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).
Recently, due to the great development of evolutionary algorithms (EA) and novel computing concepts (e.g., quantum computing concepts, chaotic mapping functions, and cloud mapping process, and so on), many advanced hybrids with those artificial-intelligence-based models are also proposed to achieve satisfactory forecasting accuracy levels. In addition, combining some superior mechanisms with an existing model could empower that model to solve problems it could not deal with before; for example, the seasonal mechanism from the ARIMA model is a good component to be combined with any forecasting models to help them to deal with seasonal problems.

School of Computer Science and Technology, Jiangsu Normal University, China.

College of Shipbuilding Engineering, Harbin Engineering University, China.

College of Mathematics & Information Science, PingDingShan University, China.

Erscheinungsdatum	05.03.2022
Mitarbeit	Gast Herausgeber: Wei-Chiang Hong, Ming-Wei Li, Guo-Feng Fan
Verlagsort	Basel
Sprache	englisch
Maße	170 x 244 mm
Themenwelt	Informatik ► Theorie / Studium ► Algorithmen
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Technik
Schlagworte	artificial neural networks (ANNs) • evolutionary algorithms • knowledge-based expert systems • Meta-heuristic Algorithms • novel intelligent technologies • seasonal mechanism • Short Term Load Forecasting • statistical forecasting models • support vector regression/support vector machines
ISBN-13	9783038975823 / 9783038975823
Zustand	Neuware