Special Issue "Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting"
A special issue of Energies (ISSN 1996-1073).
Deadline for manuscript submissions: closed (31 October 2016) | Viewed by 53058
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 Energies: Hybrid Advanced Techniques for Forecasting in Energy Sector
Special Issue in Algorithms: Advanced Hybrid 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 development of kernel methods and hybrid evolutionary algorithms (HEA) to support experts in business forecasting is of great importance to improve the accuracy of the actions derived from an energy decision maker, and that they are theoretically sound. In addition, more accurate or more precise energy demand forecasts are required while decisions are made in a competitive environment. Therefore, this is of special relevance in the big data era; these forecasts are usually based on a complex function combination. These models have resulted in over-reliance on the use of informal judgment and higher expense if lacking of ability to catch the data patterns. The novel applications of kernel methods and hybrid evolutionary algorithms can provide more satisfied parameters in forecasting models. Another issue to be addressed is that of seasonality or cyclicity of energy data, and the dynamic nonlinearity of the data in demanding process itself.
This Special Issue aims to attract researchers with an interest in the research areas described above. Specifically, we are interested in contributions towards the development of HEAs with kernel methods or with other novel methods (chaos theory, fuzzy theory, cloud theory, quantum behavior, and so on), which, with superior capabilities over the traditional optimization approaches, aims to overcome some endogenous drawbacks and then apply these new HEAs to be hybridized with original forecasting models to significantly improve forecasting accuracy. As an example, genetic algorithms with simulated annealing algorithms (GA-SA), by applying the superior capability of SA algorithm to reach more ideal solutions, and by employing the mutation process of GA to enhance the searching process. The new hybrid evolutionary algorithms require more detailed research and empirical studies. On the other hand, some other new trials, namely combined approaches, such as seasonal mechanism or multiple seasonal mechanism that are combined with forecasting models, are also welcome.
All submissions should be based on the rigorous motivation of the mentioned approaches, and all the developed models should also have a corresponding theoretical sound framework, lacking such a scientific approach is discouraged. Validation support of existing/presented approaches is encouraged to be done using real practical applications.
Prof. Dr. Wei-Chiang Hong
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Support vector regression