Guest Editors
Prof. Dr. Wei-Chiang Hong, Oriental Institute of Technology, Taiwan
Dr. Yi Liang, Hebei Geo University, China
Summary
Precise resources and energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in energy system planning, maintenance, operation, security, and so on. In the past decades, many resources and energy forecasting models have been continuously proposed to increase the forecasting accuracy, especially intelligence models (e.g., artificial neural networks, support vector regression, evolutionary computation models, etc.). Meanwhile, due to the great development of optimization methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, etc.), many novel hybrid methods combined with the above-mentioned intelligent-optimization-based methods have also been proposed to achieve satisfactory forecasting accuracy levels. It is worthwhile to explore the tendency and development of intelligent-optimization-based hybrid methodologies and to enrich their practical performances, particularly for resources and energy forecasting.
Potential topics include but are not limited to the following:
• hybrid methods
• artificial neural networks methods
• support vector regression methods
• evolutionary computation methods
• quadratic programming methods
• resources forecasting
• energy forecasting
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