Special Issue "Advanced Forecasting Methods with Applications to Smart Grids"
Deadline for manuscript submissions: 31 March 2022.
2. Department of Electrical Engineering, Aswan University, Aswan 81542, Egypt
Interests: deep learning; artificial intelligence; PV power forecasting; solar irradiance forecasting; load forecasting; wind power forecasting; renewable energy sources; image processing; computer vision; smart grid
2. Department of Electrical Engineering, Faculty of Engineering, Aswan 81542, Egypt
Interests: power systems; renewable energy sources; smart grids; distributed generation; electric vehicles; applied machine learning
Special Issues, Collections and Topics in MDPI journals
There is currently a large deployment of smart grid systems that include various renewable energy sources like photovoltaic and wind energy. These renewable energy sources could have considerable impacts on smart grid systems from both technical and environmental sides. The generated renewable energy profiles may have high daily periodicity and seasonal variations due to fluctuating weather conditions. It should be noted that the characteristics of renewable energy sources can pose ample challenges for integrating large-scale renewables in transmission systems and a high number of distributed renewables in distribution networks. Therefore, improvements in the reliability and precision of forecasting methods are needed, and it is necessary to consider the uncertainty of the data.
This Special Issue aims to present advanced forecasting methods with applications that cover diverse practical challenges in smart grid systems. The Guest Editors welcome original research as well as review articles targeting the following topics (but not limited to these):
- Forecasting methods for photovoltaic power and solar irradiance;
- Forecasting methods for wind generation systems and wind speed;
- Forecasting methods for electric vehicle charging profiles;
- Forecasting methods for load demand and consumption;
- Forecasting methods for energy prices;
- Ensemble forecasting approaches based on deep learning and metaheuristics;
- Probabilistic forecasting methods;
- Energy forecasting based on multi-source data (tabular data, images, etc.);
- Remaining useful life forecasting in smart grid systems;
- Forecasting methods with IoT data for building energy management systems;
- Sentiment analysis and forecasting methods for smart grid applications.
Dr. Mohamed Abdel-Nasser
Dr. Karar Mahmoud
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. Forecasting is an international peer-reviewed open access quarterly 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 1400 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.
- smart grid
- power systems
- PV power forecasting
- solar irradiance forecasting
- load forecasting
- wind power forecasting
- renewable energy resources
- electric vehicle
- sentimental analysis
- probabilistic forecasting