Short-Term Load Forecasting by Artificial Intelligent Technologies

Dear Colleagues,

In last few decades, short-term load forecasting (STLF) has been one of the most important research issues for the achievement of 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 many 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 (e.g., artificial neural networks (ANNs), knowledge-based expert systems, fuzzy theory and fuzzy inference systems, evolutionary computation models, support vector regression, etc.).

Recently, due to the great development of evolutionary algorithms (EAs) and novel computing concepts (e.g., quantum computing concepts, chaotic mapping functions, cloud mapping process, etc.), many advanced hybrids with those artificial-intelligence-based models are also proposed to achieve a sufficiently accurate forecasting level. In addition, combining some superior mechanism with an existing model could empower this 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 model to help them to deal with seasonal problems.

The research tedencies and development of STLF have demonstrated rich and diverse prospects, deserving of further exploration of this important issue.

All submissions should be based on the rigorous motivation of the mentioned approaches, and all the developed models should also be with a corresponding theoretically sound framework; submissions lacking such a scientific approach are discouraged. Validation of existing/presented approaches is encouraged to be done using real practical applications.

Prof. Dr. Wei-Chiang Hong
Dr. Ming-Wei Li
Dr. Guo-Feng Fan
Guest Editors

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