Special Issue "Short-Term Load Forecasting by Artificial Intelligent Technologies"
A special issue of Energies (ISSN 1996-1073).
Deadline for manuscript submissions: closed (31 October 2018).
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)
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In last few decades, short-term load forecasting (STLF) has been one of the most important research issues for the achievement of higher efficiency and reliability in power system operation, to facilitate the minimization of its operation cost by providing accurate input to day-ahead scheduling, contingency analysis, load flow analysis, planning, and maintenance of power systems. There are many forecasting models proposed for STLF, including traditional statistical models (such as ARIMA, SARIMA, ARMAX, multi-variate regression, Kalman filter, exponential smoothing, and so on) and artificial-intelligence-based models (e.g., artificial neural networks (ANNs), knowledge-based expert systems, fuzzy theory and fuzzy inference systems, evolutionary computation models, support vector regression, etc.).
Recently, due to the great development of evolutionary algorithms (EAs) and novel computing concepts (e.g., quantum computing concepts, chaotic mapping functions, cloud mapping process, etc.), many advanced hybrids with those artificial-intelligence-based models are also proposed to achieve a sufficiently accurate forecasting level. In addition, combining some superior mechanism with an existing model could empower this model to solve problems it could not deal with before; for example, the seasonal mechanism from the ARIMA model is a good component to be combined with any forecasting model to help them to deal with seasonal problems.
The research tedencies and development of STLF have demonstrated rich and diverse prospects, deserving of further exploration of this important issue.
All submissions should be based on the rigorous motivation of the mentioned approaches, and all the developed models should also be with a corresponding theoretically sound framework; submissions lacking such a scientific approach are discouraged. Validation of existing/presented approaches is encouraged to be done using real practical applications.Prof. Dr. Wei-Chiang Hong
Dr. Ming-Wei Li
Dr. Guo-Feng Fan
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 papers will be 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 2000 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.
- Short term load forecasting
- Statistical forecasting models (ARIMA; SARIMA; ARMAX; multi-variate regression; Kalman filter; exponential smoothing; and so on)
- Artificial neural networks (ANNs)
- Knowledge-based expert systems
- Fuzzy theory and fuzzy inference systems
- Evolutionary computation models
- Evolutionary algorithms
- Support vector regression (SVR)
- Hybrid models
- Combined models
- Seasonal mechanism (Single seasonal mechanism; Multiple seasonal mechanism)
- Novel intelligent technologies (Chaos theory; Cloud theory; Quantum theory)