Special Issue "Intelligent Energy Demand Forecasting"
A special issue of Energies (ISSN 1996-1073).
Deadline for manuscript submissions: closed (31 December 2011)
Prof. Dr. Wei-Chiang Hong
1. Department of Information Management, Oriental Institute of Technology, No. 58, Sec. 2, Sichuan Rd., Panchiao, Taipei, 220, Taiwan
2. Distinguished Professor at School of Education Intelligent Technology, Jiangsu Normal University, Xuzhou, China
Website | E-Mail
Interests: computational intelligence (neural networks; evolutionary computation); application of forecasting technology (ARIMA; support vector regression; chaos theory)
Dr. Yucheng Dong
Department of Organization and Management, School of Management, Xi’an Jiaotong University, China
The present issue “Intelligent Energy Demand Forecasting” focuses on accurate energy demand modeling by intelligent computation (IC) approaches to provide well energy planning, accurate energy expenditure prediction, and energy distributing efficiency. Particular forecasting technologies of this issue is concentrated on evolutionary computing, neural computing, fuzzy computing, natural computing, probabilistic computing, wavelet transform, and chaotic sequence with evolutionary algorithms, etc.. Papers are sought on recent novel IC technology developments with major application areas in (but not limited to): short term load forecasting (STLF), long term load forecasting, wind energy demand forecasting, solar energy demand forecasting, novel energy (Green energy, ocean energy, etc.) demand forecasting, and business energy demand patterns forecasting. Manuscripts on power transmission design/prediction or IC treatments of economic dispatch scheduling are not targeted in this edition and should be submitted elsewhere.
Dr. Wei-Chiang Hong,
Dr. Yucheng Dong
- short term load forecasting (STLF)
- energy demand forecasting
- intelligent computation
- evolutionary computing
- neural computing
- fuzzy computing
- natural computing
- probabilistic computing
- wavelet transform
- chaotic sequence
- evolutionary algorithms