Intelligent Energy Demand Forecasting

Dear Colleagues,

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
Guest Editors