Special Issue "Hybrid Advanced Techniques for Forecasting in Energy Sector"
A special issue of Energies (ISSN 1996-1073).
Deadline for manuscript submissions: closed (31 December 2012)
Prof. Dr. Wei-Chiang Hong (Website)
School of Economics & Management, Nanjing Tech University, Nanjing, 211800, China
Department of Information Management, Oriental Institute of Technology, No. 58, Sec. 2, Sichuan Rd., Panchiao, Taipei, 220, Taiwan
Interests: computational intelligence (neural networks; evolutionary computation); application of forecasting technology (ARIMA; support vector regression; chaos theory)
The present issue Hybrid Advanced Techniques for Forecasting in Energy Sector focuses on load/price/wind speed forecasting, which are the prime factors in modern restructured power market by any novel hybrid advanced techniques to provide significant forecasting accuracy improvements (i.e., proved by statistical test). Hybrid advanced models of this issue is not only concentrated on hybrid evolutionary algorithms or hybrid chaos theory, fuzzy theory, cloud theory with evolutionary algorithms to determine suitable parameters for an existed model, but also on hybridization of two or above existed models, such as neuro-fuzzy model, BPNN-fuzzy model, and so on.
Papers are sought on recent novel ideas by hybridizing or combining intelligent computation technologies in all fields forecasting in energy sector: genetic algorithm, simulated annealing algorithm, particle swarm optimization, ant colony optimization, immune algorithm, artificial bee colony algorithm, chaotic mapping sequence (including Logistic mapping, Cat mapping, Tent mapping, and An mapping, etc.), cloud theory, fuzzy theory, artificial neural networks, recurrent mechanism, feed forward mechanism, back-propagation mechanism, seasonal mechanism, etc..
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
- hybrid models, combined models
- energy load forecasting
- hybrid evolutionary algorithms (genetic algorithm, simulated annealing algorithm, particle swarm optimization, ant colony optimization, immune algorithm, artificial bee colony algorithm, fire fly algorithm, harmony search)
- chaotic mapping sequence (Logistic mapping, Cat mapping, Tent mapping, and An mapping)
- cloud theory
- fuzzy theory
- artificial neural networks (recurrent mechanism, feed forward mechanism, back-propagation mechanism)
- seasonal mechanism
- wavelet transform