Assoc. Prof. Dr. Yi Liang
School of Management
Hebei GEO University, China
E-mail: louisliang@hgu.edu.cn


Prof. Wei-Chiang Hong
Department of Information Management
Asia Eastern University of Science and Technology, Taiwan
E-mail: fi013@mail.aeust.edu.tw


Prof. Dongxiao Niu
School of Economics and Management
North China Electric Power University, China
E-mail: ndx@ncepu.edu.cn

Accurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of energy supply systems, and so on. In the past decades, many energy forecasting models have been continuously proposed to improve the forecasting accuracy, including traditional statistical models (e.g., ARIMA, SARIMA, ARMAX, multi-variate regression, exponential smoothing models, Kalman filtering, Bayesian estimation models, etc.) and artificial intelligence models (e.g., artificial neural networks (ANNs), knowledge-based expert systems, evolutionary computation models, support vector regression, etc.).

Recently, due to the great development of optimization modelling methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, meta-heuristic algorithms, etc.) and intelligent computing mechanisms (e.g., quantum computing, chaotic mapping, cloud mapping, seasonal mechanism, etc.), many novel hybrid models or models combined with the above-mentioned intelligent-optimization-based models have also been proposed to achieve satisfactory forecasting accuracy levels. It is worthwhile to explore the tendency and development of intelligent-optimization-based modelling methodologies and to enrich their practical performances, particularly for marine renewable energy forecasting.

All submissions should be based on the rigorous motivation of the mentioned approaches, and all the developed models should also have a corresponding theoretically sound framework. Works lacking such a scientific approach are discouraged. Validation support of existing/presented approaches is encouraged to be done using real practical applications. Potential topics include but are not limited to the following:

Submission of papers: 22 May 2022 (Hard deadline)
Notification of acceptance: 11 July 2022
Camera-ready papers: 25 July 2022
Registration & payment: 25 July 2022
Conference date: 28-30 November 2022