Special Issue "Advanced Forecasting Methods with Applications to Smart Grids"

A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Power and Energy Forecasting".

Deadline for manuscript submissions: 31 March 2022.

Special Issue Editors

Dr. Mohamed Abdel-Nasser
E-Mail Website
Guest Editor
1. Department of Computer Engineering and Mathematics, Universitat Rovira I Virgili, Av. Paisos Catalans 26, 43007 Tarragona, Spain
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
Dr. Karar Mahmoud
Guest Editor
1. Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, FI-00076 Espoo, Finland
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

Special Issue Information

Dear Colleagues,

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
Guest Editors

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
  • IoT
  • sentimental analysis
  • probabilistic forecasting

Published Papers (1 paper)

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Different Forecasting Horizons Based Performance Analysis of Electricity Load Forecasting Using Multilayer Perceptron Neural Network
Forecasting 2021, 3(4), 804-838; https://doi.org/10.3390/forecast3040049 - 02 Nov 2021
Viewed by 597
With an uninterrupted power supply to the consumer, it is obligatory to balance the electricity generated by the electricity load. The effective planning of economic dispatch, reserve requirements, and quality power provision for accurate consumer information concerning the electricity load is needed. The [...] Read more.
With an uninterrupted power supply to the consumer, it is obligatory to balance the electricity generated by the electricity load. The effective planning of economic dispatch, reserve requirements, and quality power provision for accurate consumer information concerning the electricity load is needed. The burden on the power system engineers eased electricity load forecasting is essential to ensure the enhanced power system operation and planning for reliable power provision. Fickle nature, atmospheric parameters influence makes electricity load forecasting a very complex and challenging task. This paper proposed a multilayer perceptron neural network (MLPNN) with an association of recursive fine-tuning strategy-based different forecasting horizons model for electricity load forecasting. We consider the atmospheric parameters as the inputs to the proposed model, overcoming the atmospheric effect on electricity load forecasting. Hidden layers and hidden neurons based on performance investigation performed. Analyzed performance of the proposed model with other existing models; the comparative performance investigation reveals that the proposed forecasting model performs rigorous with a minimal evaluation index (mean square error (MSE) of 1.1506 × 10−05 for Dataset 1 and MSE of 4.0142 × 10−07 for Dataset 2 concern to the single hidden layer and MSE of 2.9962 × 10−07 for Dataset 1, and MSE of 1.0425 × 10−08 for Dataset 2 concern to two hidden layers based proposed model) and compared to the considered existing models. The proposed neural network possesses a good forecasting ability because we develop based on various atmospheric parameters as the input variables, which overcomes the variance. It has a generic performance capability for electricity load forecasting. The proposed model is robust and more reliable. Full article
(This article belongs to the Special Issue Advanced Forecasting Methods with Applications to Smart Grids)
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