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) | Viewed by 108008
A printed edition of this Special Issue is available here.
Interests: short-term load forecasting; intelligent forecasting technologies (e.g., neural networks, knowledge–based expert systems, fuzzy inference systems, evolutionary computation, etc.); hybrid forecasting models (e.g., hybridizing traditional models with intelligent technologies, or hybridizing two or more different models to form a novel forecasting model); novel intelligent methodologies (chaos theory; cloud theory; quantum theory)
Special Issues, Collections and Topics in MDPI journals
Special Issue in Algorithms: Advanced Hybrid Algorithms in Energy Forecasting
Special Issue in Energies: Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting
Special Issue in Energies: Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting
Special Issue in Energies: Short-Term Load Forecasting by Artificial Intelligent Technologies
Special Issue in Forecasting: Applications of Forecasting by Hybrid Artificial Intelligent Technologies
Special Issue in Energies: Intelligent Optimization Modelling in Energy Forecasting
Special Issue in Sustainability: Advanced Intelligent Technologies in Sustainable Energy Forecasting and Economical Applications
Special Issue in Future Internet: Artificial Intelligence for Smart Cities
Special Issue in Sustainability: Carbon Neutrality: National Strategic Action Programmes and Technologies
Special Issue in Sensors: Futuristic Trends in Sensing Technologies of Digital Twin Systems
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